CN113377673B - Method, device and equipment for predicting test forward workload duty cycle of software test - Google Patents

Method, device and equipment for predicting test forward workload duty cycle of software test Download PDF

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CN113377673B
CN113377673B CN202110745708.XA CN202110745708A CN113377673B CN 113377673 B CN113377673 B CN 113377673B CN 202110745708 A CN202110745708 A CN 202110745708A CN 113377673 B CN113377673 B CN 113377673B
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software
test
independent variable
workload
sample
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CN113377673A (en
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徐雅琳
王越
曲亚南
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Agricultural Bank of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • G06F11/3672—Test management

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Abstract

The embodiment of the application provides a method, a device and equipment for predicting a test forward workload duty cycle of a software test, wherein the method comprises the following steps: acquiring an independent variable combination of a software item to be tested, wherein the independent variable combination comprises at least one independent variable which is a factor influencing the forward workload of the test; inputting the independent variable combination of the software item to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, so as to obtain the duty ratio information of the test forward movement workload of the software item to be tested; factor parameters in a preset multiple regression statistical model are obtained by adopting a plurality of sample software project data; and generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested. And automatically predicting the duty ratio of the test forward workload of the software test, and accurately predicting the duty ratio information of the test forward workload.

Description

Method, device and equipment for predicting test forward workload duty cycle of software test
Technical Field
The embodiment of the application relates to the technical field of software, in particular to a method, a device and equipment for predicting a test forward workload duty ratio of software test.
Background
In the software test, the software test needs to consume more workload to complete the whole test process, and further has the total workload of the test in the software test.
In the prior art, the amount of total workload is measured and is one standard for measuring the efficiency of software testing. However, in software testing, it may be necessary to perform test forward operations, such as software requirement development, software requirement review, and participation in code implementation. The forward test work is an important link in the whole software test; the ratio value between the test forward workload (i.e., the workload of the test forward workload) and the test total workload, i.e., the duty cycle information of the test forward workload, is also an important criterion for measuring the software test.
Therefore, a method for automatically predicting the duty ratio of the test forward workload of the software test is needed to accurately predict the duty ratio information of the test forward workload.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for predicting the duty ratio of test forward workload of software test, which are used for solving the problem of accurately predicting the duty ratio information of the test forward workload.
In a first aspect, an embodiment of the present application provides a method for predicting a test advance workload rate of a software test, where the method includes:
Obtaining an independent variable combination of a software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test;
inputting the independent variable combination of the software item to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software item to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of sample software projects and real duty ratio information of test forward workload of the sample software projects;
generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested.
In one possible embodiment, the method further comprises:
and determining the test forward workload of the software item to be tested according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and the test total workload of the software item to be tested.
In a possible implementation manner, determining the test forward workload of the software item to be tested according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and the test total workload of the software item to be tested includes:
determining a confidence interval according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and residual information obtained by processing the data of the plurality of sample software items;
and determining the test forward workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested.
In one possible embodiment, the method further comprises:
acquiring a plurality of sample software project data; wherein the plurality of sample software item data are assigned to a plurality of categories of sample software items;
and processing the initial model according to the data of the plurality of sample software items to obtain the preset multiple regression statistical model.
In a possible implementation manner, before processing the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model, the method further includes:
For each independent variable, performing multiple linear regression processing on the independent variables of the plurality of sample software project data and other independent variables of the plurality of sample software project data to obtain a decision coefficient corresponding to each independent variable;
removing the independent variable with the decision coefficient larger than a first preset threshold value from the plurality of sample software project data;
processing the independent variables of the plurality of sample software project data and the real duty ratio information of the test forward workload of the plurality of sample software project data aiming at each independent variable to obtain Pearson correlation coefficients corresponding to each independent variable;
and removing independent variables with the Pearson correlation coefficient smaller than a second preset threshold value from the plurality of sample software project data.
In a possible implementation manner, the processing the initial model according to a plurality of sample software project data to obtain the preset multiple regression statistical model includes:
inputting the multiple sample software project data subjected to the elimination processing into an initial model for processing to obtain factor parameters corresponding to each independent variable and estimated duty ratio information of test forward-shift workload of each sample software project data;
Determining a model residual error item according to the estimated duty ratio information of the test forward workload of each sample software item data and the real duty ratio information of the test forward workload of each sample software item data;
and establishing the preset multiple regression statistical model according to factor parameters corresponding to each independent variable.
In a possible implementation manner, after processing the initial model according to a plurality of sample software project data to obtain the preset multiple regression statistical model, the method further includes:
repeating the following steps until the preset multiple regression statistical model is determined to be an effective model:
constructing first statistics information according to estimated duty ratio information of test forward workload of each sample software item data, real duty ratio information of test forward workload of each sample software item data, the number of sample software item data and the number of types of independent variables;
if the value represented by the first statistic information is larger than the critical value of a first preset statistic, determining the preset multiple regression statistical model as an effective model;
if the value represented by the first statistics information is smaller than or equal to the critical value of the first preset statistics, determining the type of the replacement independent variable, and restarting the step of acquiring the data of a plurality of sample software items.
In a possible implementation manner, after processing the initial model according to a plurality of sample software project data to obtain the preset multiple regression statistical model, the method further includes:
repeating the following steps until each effective factor parameter is obtained:
determining second statistic information corresponding to each independent variable according to respective variable combinations of the plurality of sample software project data, factor parameters corresponding to each independent variable in the preset multiple regression statistical model and model residual items of the preset multiple regression statistical model;
if the value represented by the second statistic information corresponding to each independent variable is larger than the critical value of the second preset statistic, the factor parameter corresponding to the independent variable is an effective factor parameter;
if the value represented by the second statistic information corresponding to any independent variable is smaller than or equal to the critical value of the second preset statistic, eliminating the independent variable corresponding to the minimum second statistic information, and restarting executing the step of processing the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model.
In one possible embodiment, the argument is any one of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
In a second aspect, an embodiment of the present application provides a device for predicting a test advance workload rate of a software test, where the device includes:
the first acquisition unit is used for acquiring an independent variable combination of the software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test;
the prediction unit is used for inputting the independent variable combination of the software item to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and the duty ratio information of the test forward workload of the software item to be tested is obtained; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of sample software projects and real duty ratio information of test forward workload of the sample software projects;
the prompting unit is used for generating and displaying prompting information according to the duty ratio information of the test forward workload of the software item to be tested, and the prompting information is used for indicating the duty ratio information of the test forward workload of the software item to be tested.
In a possible embodiment, the apparatus further comprises:
the determining unit is used for determining the test forward workload of the software item to be tested according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and the test total workload of the software item to be tested.
In a possible embodiment, the determining unit includes:
the first determining module is used for determining a confidence interval according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and residual information obtained by processing the data of the plurality of sample software items;
and the second determining module is used for determining the test forward workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested.
In a possible embodiment, the apparatus further comprises:
a second acquisition unit configured to acquire a plurality of sample software item data; wherein the plurality of sample software item data are assigned to a plurality of categories of sample software items;
and the training unit is used for processing the initial model according to the data of the plurality of sample software items to obtain the preset multiple regression statistical model.
In a possible embodiment, the apparatus further comprises:
the first processing unit is used for performing multiple linear regression processing on the independent variables of the plurality of sample software project data and other independent variables of the plurality of sample software project data for each independent variable before the training unit processes the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model, so as to obtain a decision coefficient corresponding to each independent variable;
the first rejecting unit is used for rejecting the independent variable with the decision coefficient larger than a first preset threshold value from the plurality of sample software item data;
the second processing unit is used for processing the independent variables of the plurality of sample software project data and the real duty ratio information of the test forward-shift workload of the plurality of sample software project data aiming at each independent variable to obtain Pearson correlation coefficients corresponding to each independent variable;
and the second eliminating unit is used for eliminating the independent variable of which the Pearson correlation coefficient is smaller than a second preset threshold value from the plurality of sample software item data.
In a possible embodiment, the training unit is specifically configured to:
Inputting the multiple sample software project data subjected to the elimination processing into an initial model for processing to obtain factor parameters corresponding to each independent variable and estimated duty ratio information of test forward-shift workload of each sample software project data;
determining a model residual error item according to the estimated duty ratio information of the test forward workload of each sample software item data and the real duty ratio information of the test forward workload of each sample software item data;
and establishing the preset multiple regression statistical model according to factor parameters corresponding to each independent variable.
In a possible embodiment, the apparatus further comprises:
the first execution unit is used for processing the initial model according to the plurality of sample software project data by the training unit to obtain the preset multiple regression statistical model, and repeating the following steps until the preset multiple regression statistical model is determined to be an effective model:
constructing first statistics information according to estimated duty ratio information of test forward workload of each sample software item data, real duty ratio information of test forward workload of each sample software item data, the number of sample software item data and the number of types of independent variables;
If the value represented by the first statistic information is larger than the critical value of a first preset statistic, determining the preset multiple regression statistical model as an effective model;
and if the value represented by the first statistic information is smaller than or equal to the critical value of the first preset statistic, determining the type of the replacement independent variable, and restarting executing the second acquisition unit.
In a possible embodiment, the apparatus further comprises:
the second execution unit is used for repeating the following steps after the training unit processes the initial model according to the data of a plurality of sample software items to obtain the preset multiple regression statistical model until each effective factor parameter is obtained:
determining second statistic information corresponding to each independent variable according to respective variable combinations of the plurality of sample software project data, factor parameters corresponding to each independent variable in the preset multiple regression statistical model and model residual items of the preset multiple regression statistical model;
if the value represented by the second statistic information corresponding to each independent variable is larger than the critical value of the second preset statistic, the factor parameter corresponding to the independent variable is an effective factor parameter;
If the value represented by the second statistic information corresponding to any independent variable is smaller than or equal to the critical value of the second preset statistic, eliminating the independent variable corresponding to the minimum second statistic information, and restarting executing the training unit.
In one possible embodiment, the argument is any one of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the method of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions for performing the method of the first aspect when executed by a processor.
In a fifth aspect, embodiments of the present application provide a computer program product comprising: a computer program stored in a readable storage medium, from which it can be read by at least one processor of an electronic device, the at least one processor executing the computer program causing the electronic device to perform the method of the first aspect.
According to the method, the device and the equipment for predicting the test forward workload duty ratio of the software test, provided by the embodiment of the application, the independent variable combination of the software item to be tested is obtained, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the test forward workload; inputting independent variable combinations of the software items to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software items to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of the sample software project and real duty ratio information of test forward movement workload of the sample software project. According to factor parameters corresponding to each independent variable in a preset multiple regression statistical model, performing data processing on independent variable combinations of the software items to be tested, and generating duty ratio information of test forward workload of the software items to be tested; therefore, the duty ratio of the test forward workload of the software test is automatically predicted, and the duty ratio information of the test forward workload is accurately predicted; the duty ratio information of the test forward workload of the software item to be tested can be rapidly and effectively automatically predicted. The forward workload of the test is predicted and analyzed, so that the rationality of the test requirement can be verified, the efficiency of software research and development is improved, and the cost of software research and development is reduced; and the method is more beneficial to realizing the automation of software testing and promoting the development and application of software. Generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested; and then automatically reminding a tester to check the duty ratio of the test forward workload of the software project to be tested, so as to be convenient for adjusting the software project.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart of a method for predicting a test forward workload rate for a software test according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for predicting a test advance workload rate for another software test according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a device for predicting a test forward workload rate of a software test according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a device for predicting a test forward workload rate for another software test according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
In the software test, the software test needs to consume more workload to complete the whole test process, and further has the total workload of the test in the software test. In the software testing process, the software project demand quality is improved, the research and development process is optimized, the research and development cost is reduced, the testing value is realized, and the like, so that a tester is required to enter the software project testing process as soon as possible, and therefore, the testing work is required to advance to the software demand development, the software demand review and the software participation coding implementation stage, and the time point of quality assurance is advanced. Thus, a test advance and a test advance workload are generated, the test advance workload being a part of the total workload of the test.
In one example, how much the total workload is tested is one criterion for measuring the efficiency of the software test. However, in software testing, it may be necessary to perform test forward operations, such as software requirement development, software requirement review, and participation in code implementation. The forward test work is an important link in the whole software test; the ratio value between the test advance workload (i.e., the workload of the test advance workload) and the test total workload, i.e., the duty cycle information of the test advance workload (which may also be referred to as the duty cycle of the test advance workload), is also an important criterion for measuring the software test. If the workload of the test forward movement is relatively low, it is indicated that the testers may not know the software project sufficiently, and a great deal of workload is required for subsequent software tests; the workload of the test forward movement is too much higher, which means that the workload of the test forward movement is higher, resulting in lower overall software test efficiency.
In one example, the duty cycle of the test advance workload of the software project can be calculated by manual mode according to manual experience estimation. However, in the manual estimation mode, because the test of the software project is affected by various factors, the duty ratio of the obtained forward workload of the test is inaccurate, and subjective error judgment is easy to occur in the manual mode; even, there may be a case where the estimated duty ratio of the test advance workload is greatly different from the actual duty ratio of the test advance workload.
Therefore, a method for automatically predicting the duty ratio of the test forward workload of the software test is needed to accurately predict the duty ratio information of the test forward workload.
The embodiment of the application provides a method, a device and equipment for predicting the test forward workload duty ratio of a software test, which aim to solve the technical problems in the prior art.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for predicting a test forward workload rate of a software test according to an embodiment of the present application, as shown in fig. 1, where the method includes:
101. and obtaining an independent variable combination of the software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test.
In one example, the argument is any one of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
The execution body of the present embodiment is, for example, an electronic device, or a terminal device, or a server, or a prediction device or device of a test advance workload ratio of a software test, or other devices or devices that can execute the embodiment, which is not limited thereto.
And acquiring data of the software item to be tested, wherein the data comprises an independent variable combination, and the independent variable combination comprises at least one independent variable. Each argument is a factor that affects the test advance workload. According to various indexes of the software project to be tested, respective variables can be determined.
The data of the software item to be tested can be read from other equipment; or receiving data of the software items to be tested, which are sent by other equipment.
The argument may be any of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects. The plurality of independent variables form an independent variable combination.
The software project scale information is characterized in that various workloads of the software project can be quantified, for example, development work of the software project is quantified, and test work of the software project is quantified (for example, quantification is performed in a unit of 'people and months'); and then the total workload of the software project can be obtained; the larger the total workload of the software project is, the larger the workload of the software test project in the software project is, the larger the workload of the test forward is; the total workload of a software project may be referred to as software project size information.
The internal collaboration complexity is characterized in that the number of each internal department participating in the sponsorship of the software test project can be quantified; the more the number of the internal departments is, the more difficult the personnel scheduling is, and the more the required test forward-moving workload is; the number of internal departments may be referred to as the internal collaborative complexity.
External collaboration complexity, characterized by the number of external collaboration units participating in a software test item (i.e., the number of external collaboration scenarios) can be quantified; external collaboration units, for example, purchasing systems, corporate collaboration, collaboration with overseas institutions, and the like; the number of external coordination units may be referred to as the external coordination complexity. For example, if there is no external collaboration situation, the external collaboration complexity takes a value of 0; if two external collaboration situations exist, the external collaboration complexity has a value of 2 (for example, a purchasing system exists in the software test item and a total company collaboration exists, the external collaboration complexity has a value of 2).
A software project review level, characterized in that the software project can be set in the review level. For example, the review level of a software project (which includes three processes of software requirements, software development, and software testing; but these three processes may run throughout the implementation of the software project) is divided into three levels.
The system importance degree of the software project is characterized in that the importance degree of the software project can be set in a level. For example, the importance of a system of software items is divided into five importance levels.
The system relation of the software items is characterized in that the new interface consumption relation coefficient of the software items can be quantified, and the new interface consumption relation coefficient is called as the system relation of the software items. The more the number of the new interface consumption relation coefficient is, the more interfaces to be tested are represented, and the more the required test forward-moving workload is increased.
The system deployment scale information of the software project is characterized in that the total number of newly added computing resources of the software project can be quantified, and the total number of the newly added computing resources is called as the system deployment scale information of the software project. The more the total number of the newly added computing resources is, the more complex the software test project is, the problems of performance test necessity and the like need to be analyzed, and the more the required test forward workload is.
The number of test advance participation phases of the item of software characterizes the number of phases for which a test advance job needs to be deployed. For example, the test forward work can be performed in four stages of software demand development, software demand review, software participation coding implementation and software unit and integrated test, and the larger the number of stages required to perform the test forward work, the larger the test forward work load is required.
102. Inputting independent variable combinations of the software items to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software items to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of the sample software project and real duty ratio information of test forward movement workload of the sample software project.
Illustratively, it is known from the foregoing that the duty cycle of the test advance workload is an important indicator to be predicted. Test advance, also called test left shift; the forward test refers to early intervention of a tester in the process of developing a software product, advances software testing work to a stage of demand development, demand review and participation in coding implementation, and forward a time point of software quality assurance.
And training the initial model by adopting a plurality of sample software project data in advance, so as to obtain a preset multiple regression statistical model. Each sample software item data comprises independent variable combinations of the sample software items and real duty ratio information of test forward workload of the sample software items. Wherein the types of the independent variables in the independent variable combination of the data of each sample software item and the independent variable types in the independent variable combination of the software item to be tested are the same. The real duty ratio information of the test forward workload of the sample software item is a value of the real duty ratio of the test forward workload of the sample software item.
After training the initial model, the obtained preset multiple regression statistical model has factor parameters corresponding to each independent variable. For example, a sample software item data is obtained, a is a positive integer greater than or equal to 1, each sample software item data seed includes B independent variables, B is a positive integer greater than or equal to 1, and a is greater than or equal to B; after training the initial model by adopting A sample software project data, the obtained preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable in B independent variables.
Wherein, a multiple regression statistical model, namely, a multiple regression analysis model is adopted in the application. Regression analysis, which means that the main object of study is the statistical relationship between objective things variables; and further, by establishing a statistical model, the degree of closeness and structural state of the interrelationships among the variables are analyzed, and model prediction is carried out. Multiple regression analysis refers to treating one of the relevant variables as a dependent variable and the other variables as independent variables (independent variables, which may also be referred to as factors); and then establishing a linear or nonlinear mathematical model number relation between the plurality of variables (i.e., establishing a linear mathematical model number relation between the dependent variable and the independent variable, or establishing a nonlinear mathematical model number relation between the dependent variable and the independent variable), and then performing statistical analysis of the analysis by using the sample data. Dependent variable, refers to the duty cycle of the test advance workload.
Inputting the independent variable combination of the software item to be tested in the step 101 into a preset multiple regression statistical model, so as to run the multiple regression statistical model, and processing the data of the software item to be tested according to factor parameters corresponding to each independent variable in the multiple regression statistical model to obtain the duty ratio information of the test forward workload of the software item to be tested, namely, the duty ratio of the test forward workload of the software item to be tested (namely, the ratio between the test forward workload of the software item to be tested and the test total workload of the software item to be tested).
103. Generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested.
For example, after obtaining the duty ratio information of the test advance workload of the software item to be tested, a prompt message may be generated and displayed. For example, the prompt information is text information, the prompt information is pop-up box, and the prompt information is voice information. And further displaying the duty ratio of the test forward workload of the software item to be tested for the tester. If the workload of the test forward movement is low, the test staff may not know the software project sufficiently, which causes that a great deal of workload is required for the subsequent software test; if the workload of the test forward movement is too much higher, the workload of the test forward movement is higher, so that the overall software test efficiency is lower.
In this embodiment, an independent variable combination of a software item to be tested is obtained, where the independent variable combination includes at least one independent variable, and the independent variable is a factor affecting the forward workload of the test; inputting independent variable combinations of the software items to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software items to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of the sample software project and real duty ratio information of test forward movement workload of the sample software project. According to factor parameters corresponding to each independent variable in a preset multiple regression statistical model, performing data processing on independent variable combinations of the software items to be tested, and generating duty ratio information of test forward workload of the software items to be tested; therefore, the duty ratio of the test forward workload of the software test is automatically predicted, and the duty ratio information of the test forward workload is accurately predicted; the duty ratio information of the test forward workload of the software item to be tested can be rapidly and effectively automatically predicted. The forward workload of the test is predicted and analyzed, so that the rationality of the test requirement can be verified, the efficiency of software research and development is improved, and the cost of software research and development is reduced; and the method is more beneficial to realizing the automation of software testing and promoting the development and application of software. Generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested; and then automatically reminding a tester to check the duty ratio of the test forward workload of the software project to be tested, so as to be convenient for adjusting the software project.
Fig. 2 is a flowchart of another method for predicting a test advance workload rate of a software test according to an embodiment of the present application, as shown in fig. 2, where the method includes:
201. acquiring a plurality of sample software project data; wherein the plurality of sample software item data belong to a plurality of kinds of sample software items.
The execution body of the present embodiment is, for example, an electronic device, or a terminal device, or a server, or a prediction device or device of a test advance workload ratio of a software test, or other devices or devices that can execute the embodiment, which is not limited thereto.
And acquiring a plurality of sample software item data, wherein each sample software item data comprises independent variable combinations of the sample software items and real duty ratio information of test forward workload of the sample software items. Wherein the types of the independent variables in the independent variable combination of the data of each sample software item and the independent variable types in the independent variable combination of the software item to be tested are the same.
For each sample software project data, m independent variables are provided, and m is a positive integer greater than or equal to 1; m types of independent variables constitute independent variable combinations (x 1 ，x 2 ，...，x m ). It is known that factors affecting test advance workload distribution are converted into arguments that can be quantified.
And, the number of pieces of sample software item data is larger than the number m of kinds of arguments. And the more the number of sample software project data is, the better the effect of the preset multiple regression statistical model obtained through training is ensured. For example, if the number of types of independent variables m=8, the number of sample software project data is greater than or equal to 8 to ensure that the factor parameters corresponding to each independent variable can be obtained to ensure the training effect of the multiple regression statistical model.
Descriptive statistical analysis is carried out on the real duty ratio information of various independent variables and sample software project data, and statistical characteristics of the real duty ratio information of the respective variables and the sample software project data are primarily mastered. For example, a plurality of sample software item data are assigned to a plurality of kinds of sample software items; wherein the number of sample software item data under each kind of sample software item is similar. And further, the types of sample software project data are required to be dispersed (namely, sample software project data are required to be present in each sample software project type), but the quantity of the sample software project data is not required to be dispersed, and the obtained preset multiple regression statistical model is ensured to be applicable to various software projects to be tested.
Wherein the duty cycle of the test advance workload is a dependent variable.
202. For each independent variable, performing multiple linear regression processing on the independent variables of the plurality of sample software project data and other independent variables of the plurality of sample software project data to obtain a decision coefficient corresponding to each independent variable; and removing the independent variable with the decision coefficient larger than a first preset threshold value from the plurality of sample software item data.
Illustratively, after step 201, correlations between the respective variables and observations of the dependent variables (the observations of the dependent variables at this time, which are the actual duty cycle information of the test advance workload) may be analyzed to further reject inappropriate independent variables (i.e., reject one or more independent variables). Dependent variable, refers to the duty cycle of the test advance workload. The observation value of the dependent variable is the real duty ratio information of the forward workload of the test; the estimated value of the dependent variable is estimated duty ratio information of the test forward workload.
First, the correlation between the respective variables may be analyzed. At this time, the correlation between the independent variables is analyzed, and then the multiple collinearity is analyzed. Because the application also interprets the meaning of the multi-line statistical model besides predicting the duty ratio information of the forward workload of the test, the obvious correlation (namely, analysis decision coefficient) between the respective variables needs to be analyzed, and the inaccuracy of the obtained factor parameters (but the accuracy of the dependent variables is not affected) is avoided.
For the j-th independent variable x j J is a positive integer greater than or equal to 1 and less than or equal to m, and the respective variables x of the plurality of sample software item data are calculated j Performing multiple linear regression processing with other (m-1) independent variables of multiple sample software project data to obtain a j independent variable x j Corresponding determination coefficient
And the j-th independent variable x j Corresponding determination coefficientThe larger the value, the more (m-1) independent variables are characterized, the j-th independent variable x can be better explained j I.e. characterising the j-th argument x j Multiple collinearity exists with other arguments. Thus, if it is related to the jth argument x j Corresponding determination coefficient->Greater than a first preset threshold (the first preset threshold is a preset value), the j-th independent variable x is obtained j Culling from a plurality of sample software project data.
203. Processing the independent variables of the plurality of sample software project data and the real duty ratio information of the test forward workload of the plurality of sample software project data aiming at each independent variable to obtain Pearson correlation coefficients corresponding to each independent variable; and removing independent variables with the Pearson correlation coefficient smaller than a second preset threshold value from the plurality of sample software project data.
For example, observations of the respective variables and dependent variables (observations of the dependent variables at this time, real duty cycle information of test advance workload) may be analyzed. And analyzing the correlation between the respective variables and the observed values of the dependent variables one by one, and screening out the independent variables with obvious correlation with the test forward workload duty ratio.
Thus, for the jth argument x j J is a positive integer greater than or equal to 1 and less than or equal to m, and the respective variables x of the plurality of sample software item data are calculated j Calculating the real duty ratio information of the test forward workload of the data of a plurality of sample software items to obtain the j-th independent variable x j Corresponding Pearson correlation coefficients. And the j-th independent variable x j The smaller the corresponding Pearson correlation coefficient, the more j-th independent variable x is characterized j The correlation with the dependent variable is not obvious, and the j-th independent variable x needs to be removed j 。
If and j th independent variable x j If the corresponding Pearson correlation coefficient is smaller than a second preset threshold (the second preset threshold may be a preset value), determining the jth independent variable x j The correlation with the dependent variable is not obvious, and the j-th independent variable x j Culling from a plurality of sample software project data. Wherein the second preset threshold may be 0.3.
204. And processing the initial model according to the data of the plurality of sample software items to obtain a preset multiple regression statistical model.
In one example, step 204 specifically includes the steps of:
inputting the multiple sample software project data subjected to the elimination processing into an initial model for processing to obtain factor parameters corresponding to each independent variable and estimated duty ratio information of test forward-shift workload of each sample software project data; determining a model residual error item according to the estimated duty ratio information of the test forward workload of each sample software item data and the real duty ratio information of the test forward workload of each sample software item data; and establishing a preset multiple regression statistical model according to factor parameters corresponding to each independent variable.
Illustratively, after step 203, the obtained plurality of sample software project data are input into an initial model for calculation processing, so as to establish a preset multiple regression statistical model.
First, after step 203, the independent variables are subjected to a culling process, and p kinds of independent variables remain. p is a positive integer of 1 or more, and p is m or less.
In this step, it is necessary to determine the factor parameter corresponding to each independent variable, i.e., the jth independent variable x j Corresponding factor parameter beta j . The set of factor parameters is (beta) 1 ，β 2 ，...，β j ，...，β p ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein p is a positive integer greater than or equal to 1; j is a positive integer of 1 or more and p or less.
And, have p kinds of independent variables in each sample software project data; p types of independent variables constitute independent variable combinations (x 1 ，x 2 ，...，x j ，...，x p ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein p is a positive integer greater than or equal to 1; j is a positive integer of 1 or more and p or less.
Further, an initial model y=β is established 0 +β 1 x 1 +β 2 x 2 +…+β j x j +…+β p x p +ε. y is the dependent variable described above, i.e., y is the duty cycle of the test advance workload. Wherein the constant term beta 0 The set of each factor parameter is (β 1 ，β 2 ，...，β j ，...，β p ) Parameters to be estimated; epsilon is a model residual term and epsilon is used to represent the portion of the duty cycle of the test advance workload that cannot be interpreted by the argument.
For example, the processed multiple sample software project data is culled with 8 dependent variables. Then x 1 Characterizing software project size information, x 2 Characterization of internal synergistic complexity, x 3 Characterizing a software project review level, x 4 Characterization of external synergistic complexity, x 5 System importance, x, for characterizing software items 6 Characterizing inter-system relationships of software items, x 7 System deployment scale information, x, characterizing a software project 8 The number of test advance participation phases characterizing the item of software.
The plurality of sample software item data after the elimination processing is n sample software item data. The multiple regression statistical model (i.e., multiple linear regression model) may in turn be expressed as:
/>
wherein n is a positive integer of 1 or more, and n is p or more. i is a positive integer of 1 or more and n or less. y is i The duty cycle of the test advance workload for the ith sample software item data (at this time, y i Real duty cycle information of test advance workload for the ith sample software item data). X is x ij Is the jth argument in the ith sample software item data.
The multiple regression statistical model is processed into a matrix form y=xc+e. X is a matrix formed by all independent variables in the multiple regression statistical model. C is a matrix formed by all factor parameters in the multiple regression statistical model, and C can also be expressed as beta; e is a matrix formed by all model residual terms in the multiple regression statistical model, and E can also be expressed as E. Y may also be denoted Y.
The least square estimation of the regression parameters of the multiple regression statistical model is that
Thus, inputting a plurality of pieces of sample software project data subjected to the elimination processing into an initial model for processing; further, according to the multiple regression statistical model, a constant term beta can be obtained by performing calculation processing based on least square estimation (least square estimation can minimize the sum of squares of model residual terms) 0 Factor parameter beta corresponding to each independent variable j (i.e., a set of factor parameters (beta) 1 ，β 2 ，...，β j ，...，β p ) And estimated duty cycle information for the test advance workload for each sample item of software data.
Since the real duty ratio information of the test forward workload of each sample software item data is known, the estimated duty ratio information of the test forward workload of each sample software item data can be obtained through the process. The difference between the estimated duty cycle information of the test advance workload of each sample software item data and the actual duty cycle information of the test advance workload of each sample software item data can be calculated, and then the model residual item epsilon is determined according to each difference.
Thus, the factor parameter beta corresponding to each independent variable is obtained j (i.e., a set of factor parameters (beta) 1 ，β 2 ，...，β j ，...，β p ) A preset multiple regression statistical model for predicting the duty cycle of the test advance workload may be established.
In one example, following step 204, the following steps may also be performed.
And repeating the following second step to fourth step until the preset multiple regression statistical model is determined to be an effective model.
And a second step of constructing first statistics information according to the estimated duty ratio information of the test forward workload of each sample software item data, the real duty ratio information of the test forward workload of each sample software item data, the number of the sample software item data and the number of the types of the independent variables.
And thirdly, if the numerical value represented by the first statistic information is larger than the critical value of the first preset statistic, determining the preset multiple regression statistic model as an effective model.
And a fourth step of determining the type of the replacement argument if the value represented by the first statistics is less than or equal to the critical value of the first preset statistics, and restarting the execution of step 201.
The following steps may also be performed.
And fifth, repeating the following sixth to eighth steps until each effective factor parameter is obtained.
And a sixth step of determining second statistic information corresponding to each independent variable according to respective variable combinations of the plurality of sample software project data, factor parameters corresponding to each independent variable in a preset multiple regression statistical model and model residual items of the preset multiple regression statistical model.
And a seventh step, if the value represented by the second statistic information corresponding to each independent variable is larger than the critical value of the second preset statistic, the factor parameter corresponding to the independent variable is an effective factor parameter.
And an eighth step of eliminating the argument corresponding to the minimum second statistic information if the value represented by the second statistic information corresponding to any argument is less than or equal to the critical value of the second preset statistic, and restarting the execution of step 204.
Illustratively, after step 204, the effect of the multiple regression statistical model needs to be verified, i.e., the significance test of the regression equation and the significance test of the factor parameters are performed.
First, it is verified that the regression equation is checked for significance (i.e., the significance level is checked). Significance level refers to the probability that an estimated overall parameter falls within a certain interval, i.e., 1-confidence, that an error is likely to be made.
The first statistical information F may be constructed by an analysis of variance table with estimated duty information of the test advance workload of each sample software item data, and real duty information of the test advance workload of each sample software item data, the number of sample software item data, and the number of kinds of independent variables. Then, assume H 0 ：β 1 ＝β 2 ＝…＝β j ＝…＝β p ＝0，H 1 : not all beta j Are all 0. If the calculated value represented by the first statistic information F is larger than the critical value of the first preset statistic, determining the obtained preset multiple regression statistic model as an effective model, wherein the first preset statistic represents a function related to sample software project data; at this time, hypothesis H is rejected 0 It is determined that at the preset confidence α, the duty cycle of the test advance workload of the sample software project data has a significant linear relationship with the respective variable, i.e., the dependent variable has a significant linear relationship with the respective variable. If the calculated value represented by the first statistics information F is smaller than or equal to the critical value of the first preset statistics, acceptingSuppose H 0 Determining that the obtained preset multiple regression statistical model is low in effectiveness, namely that the obtained preset multiple regression statistical model is unavailable; at this point, execution of step 201 may be resumed after the type of argument is replaced (i.e., the model training is resumed again using the sample software project data with the type of argument replaced), or another method may be used to calculate the respective variables. Repeating the above process until the obtained preset multiple regression statistical model is determined to be an effective model.
A significance test of the factor parameter may then be performed. Wherein the constant term beta 0 No verification may be performed.
Constructing statistic information according to respective variable combinations of a plurality of sample software item data, factor parameters corresponding to each independent variable in the obtained preset multiple regression statistical model, model residual items of the obtained preset multiple regression statistical model, the number of the sample software item data and the number of the types of the independent variablesFurther determine the independent variable x with j j Corresponding second statistics information |t j | a. The invention relates to a method for producing a fibre-reinforced plastic composite. Wherein c jj For the matrix (X' X) -1 Elements on the j-th column of the j-th row; sigma is the standard deviation of the model residual terms of the obtained preset multiple regression statistical model. Suppose H 0j ：β j =0, wherein p is a positive integer of 1 or more; j is a positive integer of 1 or more and p or less. If the independent variable x of the j th type is determined j Corresponding second statistics information |t j The value of the second preset statistic is larger than the critical value of the second preset statistic, wherein the critical value of the second preset statistic can be a preset value +.>Then the current jth argument x is determined j Corresponding factor parameter beta j Is an effective factor parameter; at this time reject hypothesis H 0j Factor parameters specifying the argument at this time are available, determined at the time of the preliminary Under the setting of the confidence alpha, the jth independent variable x j Has a significant effect on the duty cycle (i.e., dependent variable) of the test advance workload. If the independent variable x of the j th type is determined j Corresponding second statistics information |t j I, less than or equal to the critical value of the second preset statistic, can temporarily reserve the j-th independent variable x j The method comprises the steps of carrying out a first treatment on the surface of the Alternatively, in the case where there is the second statistic information corresponding to the independent variable being equal to or less than the critical value of the second preset statistic (i.e., the value represented by the second statistic information corresponding to any independent variable is equal to or less than the critical value of the second preset statistic), the independent variables of which the second statistic information is equal to or less than the critical value of the second preset statistic are not eliminated, but the independent variable corresponding to the minimum second statistic information is eliminated due to the interaction between the independent variables, and then the execution of step 204 is restarted. Repeating the above process until each effective factor parameter is obtained.
205. And obtaining an independent variable combination of the software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test.
In one example, the argument is any one of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
Illustratively, this step may refer to step 101 in fig. 1, and will not be described in detail.
206. Inputting independent variable combinations of the software items to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software items to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of the sample software project and real duty ratio information of test forward movement workload of the sample software project.
Illustratively, this step may refer to step 102 of fig. 1, and will not be described in detail.
207. Generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested.
Illustratively, this step may refer to step 103 of fig. 1, and will not be described in detail.
And, after step 204, obtaining a preset multiple regression statistical model The respective independent variables x at this time j Is to be inputted, each factor parameter +. >Obtained after step 204. />The subscript of (1) is j. The respective variables of the software items to be tested are input into a preset multiple regression statistical model, so that the duty ratio information of the test forward workload of the software items to be tested (namely, the estimated duty ratio information of the test forward workload of the software items to be tested) can be obtained, wherein the duty ratio information of the test forward workload of the software items to be tested has the values of [0,1]Between them. The duty ratio information of the test forward workload of the software project to be tested is used for guiding testers to reasonably distribute the work tasks of each link of the software test.
In one example, the obtained argument in the preset multiple regression statistical model includes software project size information x 1 Internal collaborative complexity x 2 Review level x of software project 3 . Acquiring a software item to be tested, wherein the independent variable combination of the software item to be tested comprises software item scale information x 1 Internal collaborationComplexity x 2 Review level x of software project 3 The method comprises the steps of carrying out a first treatment on the surface of the And inputting the respective variables of the software item to be tested into the obtained preset multiple regression statistical model, and obtaining the duty ratio information of the test forward workload of the software item to be tested.
Wherein if the internal cooperative complexity x is maintained 2 And software project review level x 3 Unchanged, software item scale information x 1 Every 1 month, the duty cycle of the test forward workload needs to be increasedIf the software project scale information x is maintained 1 And software project review level x 3 Unchanged, then the internal collaborative complexity x 2 For every 1 increase (i.e. the number of internal departments participating in the project co-ordination), the duty cycle of the test advance workload needs to be increased +.>If the software project scale information x is maintained 1 And internal collaborative complexity x 2 Unchanged, software project review level x 3 The duty cycle of the test advance workload needs to be increased every time one level is reduced (e.g., from a first level to a second level of review)>Wherein if->And negative, the duty cycle of the test advance workload is reduced.
208. And determining the test forward workload of the software item to be tested according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and the test total workload of the software item to be tested.
In one example, step 208 specifically includes the steps of:
determining a confidence interval according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and residual information obtained by processing the data of a plurality of sample software items; and determining the test forward workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested.
For example, after the duty ratio information of the test forward workload of the software item to be tested is obtained, the test forward workload of the software item to be tested may be calculated according to the duty ratio information of the test forward workload, the preset confidence α, and the test total workload of the software item to be tested. The unit of the test forward workload can be 'human month'.
Where confidence means that the conclusion is always uncertain when the sample makes an estimate of the overall parameter due to the randomness of the sample. Therefore, a probabilistic statement method is adopted, and confidence is used to represent how large the probability that the estimated value and the overall parameter are within a certain allowable error range.
In one example, the duty cycle information of the test advance workload of the software item to be tested is estimatedThe confidence interval is determined by presetting the confidence α (at this time, the confidence may be set to 1- α), and residual information obtained by processing a plurality of sample software item data. For example, the confidence interval is +.>Can also be characterized as +.>e(ε 0 ) Characterizing a numerical value associated with the model residual term. />Is a preset value. Confidence interval, characterized by an estimated interval of overall parameters constructed from the sample statistics.
And then, multiplying the left and right boundaries of the confidence interval by the total workload of the software item to be tested to obtain a value interval of the forward workload of the software item to be tested.
A time window can be set, then the latest sample software project data is adopted regularly, steps 201-204 are executed, and then a multiple regression statistical model is obtained through training, so that the effectiveness of the model is ensured; and adopting the steps 205-208 to test the latest software item to be tested by using a multiple regression statistical model to obtain the duty ratio of the test forward workload of the software item to be tested and the value interval of the test forward workload of the software item to be tested.
In this embodiment, on the basis of the foregoing embodiment, multiple linear regression processing is performed on the independent variables of the plurality of sample software item data and other independent variables of the plurality of sample software item data, and the independent variables with the decision coefficients greater than the first preset threshold are removed, so as to analyze the correlation between the respective variables, and avoid inaccuracy of the obtained factor parameters; processing independent variables of the plurality of sample software project data and real duty ratio information of test forward workload of the plurality of sample software project data, removing independent variables with Pearson correlation coefficient smaller than a second preset threshold value, analyzing correlation relations between the independent variables and dependent variables (the dependent variables at the moment are real duty ratio information of the test forward workload), and selecting independent variables with obvious correlation relations with the test forward workload duty ratio. And the respective variables provided by the application are all factors which can influence the forward workload of the test, the factors are converted into quantifiable index factors, and then the relevant data are collected for statistical analysis, and the rejecting processing is repeated to obtain the independent variables which are more relevant to the forward workload of the test. Then processing the initial model according to the sample software project data subjected to the rejection processing to obtain a preset multiple regression statistical model; the precision and the prediction accuracy of the model are ensured. The significance test of the regression equation and the significance test of the factor parameters can be performed, so that the effect of the multiple regression statistical model and the effectiveness of each factor parameter in the multiple regression statistical model are ensured. The factor parameters of the provided multiple regression statistical model can be interpreted and the error can be measured. Meanwhile, screening and analysis of independent variables are completed through a large amount of sample software project data, and errors caused by randomness are reduced. Predicting the software item to be tested according to the multiple regression statistical model to obtain the duty ratio of the test forward workload of the software item to be tested and the value interval of the test forward workload of the software item to be tested, and automatically and accurately completing the prediction of the test forward workload; the method provides a basis for reasonably distributing the time of the test forward work for the testers, and improves the software test efficiency and quality. Inaccuracy and subjectivity of manual estimation of the test forward workload are avoided, and prediction speed and prediction accuracy of the test forward workload are effectively improved. A time window may be set, and then the latest sample software project data is adopted periodically to execute steps 201-204, so as to train and obtain a multiple regression statistical model, and ensure the effectiveness of the model.
Fig. 3 is a schematic structural diagram of a device for predicting a test forward workload rate of a software test according to an embodiment of the present application, where, as shown in fig. 3, the device includes:
the first obtaining unit 31 is configured to obtain an argument combination of a software item to be tested, where the argument combination includes at least one argument, and the argument is a factor affecting a workload of forward test.
The prediction unit 32 is configured to input the independent variable combinations of the software items to be tested into a preset multiple regression statistical model, where the preset multiple regression statistical model has factor parameters corresponding to each independent variable, so as to obtain duty ratio information of the test forward movement workload of the software items to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of the sample software project and real duty ratio information of test forward movement workload of the sample software project.
The prompting unit 33 is configured to generate and display prompting information according to the duty ratio information of the test forward workload of the software item to be tested, where the prompting information is used to indicate the duty ratio information of the test forward workload of the software item to be tested.
For example, the present embodiment may refer to the above method embodiment, and the principle and technical effects thereof are similar, and will not be described again.
Fig. 4 is a schematic structural diagram of another device for predicting a test forward workload rate for software testing according to an embodiment of the present application, and based on the embodiment shown in fig. 3, as shown in fig. 4, the device further includes:
the determining unit 41 is configured to determine a test forwarding workload of the software item to be tested according to the duty ratio information of the test forwarding workload of the software item to be tested, the preset confidence, and the test total workload of the software item to be tested.
In one example, the determining unit 41 includes:
the first determining module 411 is configured to determine a confidence interval according to duty ratio information of a test advance workload of a software item to be tested, a preset confidence, and residual information obtained by processing a plurality of sample software item data.
The second determining module 412 is configured to determine a test forwarding workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested.
In one example, the apparatus provided in this embodiment further includes:
a second acquisition unit 42 for acquiring a plurality of sample software item data; wherein the plurality of sample software item data belong to a plurality of kinds of sample software items.
The training unit 43 is configured to process the initial model according to the plurality of sample software project data, and obtain a preset multiple regression statistical model.
In one example, the apparatus provided in this embodiment further includes:
the first processing unit 44 is configured to perform multiple linear regression processing on the independent variables of the plurality of sample software item data and other independent variables of the plurality of sample software item data for each independent variable before the training unit 43 processes the initial model according to the plurality of sample software item data to obtain a preset multiple regression statistical model, so as to obtain a decision coefficient corresponding to each independent variable.
The first rejecting unit 45 is configured to reject, from the plurality of sample software item data, an argument whose decision coefficient is greater than a first preset threshold value.
The second processing unit 46 is configured to process, for each argument, the arguments of the plurality of sample software item data and the real duty ratio information of the test advance workload of the plurality of sample software item data, to obtain Pearson correlation coefficients corresponding to each argument.
And a second rejecting unit 47, configured to reject the independent variable whose Pearson correlation coefficient is smaller than the second preset threshold value from the plurality of sample software item data.
In one example, training unit 43 is specifically configured to:
inputting the multiple sample software project data subjected to the elimination processing into an initial model for processing to obtain factor parameters corresponding to each independent variable and estimated duty ratio information of test forward-shift workload of each sample software project data; determining a model residual error item according to the estimated duty ratio information of the test forward workload of each sample software item data and the real duty ratio information of the test forward workload of each sample software item data; and establishing a preset multiple regression statistical model according to factor parameters corresponding to each independent variable.
In one example, the apparatus provided in this embodiment further includes:
the first execution unit 48 is configured to repeat the following steps after the training unit 43 processes the initial model according to the plurality of sample software project data to obtain a preset multiple regression statistical model, until the preset multiple regression statistical model is determined to be an effective model: constructing first statistics information according to estimated duty ratio information of test forward workload of each sample software item data, real duty ratio information of test forward workload of each sample software item data, the number of sample software item data and the number of types of independent variables; if the value represented by the first statistic information is larger than the critical value of the first preset statistic, determining a preset multiple regression statistic model as an effective model; if the value represented by the first statistics is less than or equal to the threshold value of the first preset statistics, the type of the replacement argument is determined, and execution of the second acquisition unit 42 is restarted.
In one example, the apparatus provided in this embodiment further includes:
the second execution unit 49 is configured to repeat the following steps after the training unit 43 processes the initial model according to the plurality of sample software project data to obtain a preset multiple regression statistical model, until each effective factor parameter is obtained: determining second statistic information corresponding to each independent variable according to respective variable combinations of the plurality of sample software project data, factor parameters corresponding to each independent variable in a preset multiple regression statistical model and model residual items of the preset multiple regression statistical model; if the value represented by the second statistic information corresponding to each independent variable is larger than the critical value of the second preset statistic, the factor parameter corresponding to the independent variable is an effective factor parameter; if the value represented by the second statistic information corresponding to any one of the independent variables is smaller than or equal to the critical value of the second preset statistic, the independent variable corresponding to the minimum second statistic information is eliminated, and the training unit 43 is restarted.
In one example, the argument is any one of the following: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
For example, the present embodiment may refer to the above method embodiment, and the principle and technical effects thereof are similar, and will not be described again.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where, as shown in fig. 5, the electronic device includes: a memory 71, a processor 72;
a memory 71; a memory for storing instructions executable by processor 72;
wherein the processor 72 is configured to perform the method as provided in the above embodiments.
The electronic device further comprises a receiver 73 and a transmitter 74. The receiver 73 is for receiving instructions and data transmitted from an external device, and the transmitter 74 is for transmitting instructions and data to an external device.
Fig. 6 is a block diagram of an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, etc., provided in an embodiment of the present application.
The apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the apparatus 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 806 provides power to the various components of the device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 800.
The multimedia component 808 includes a screen between the device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the apparatus 800 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the apparatus 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the assemblies, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in position of the device 800 or one of the assemblies of the device 800, the presence or absence of user contact with the device 800, an orientation or acceleration/deceleration of the device 800, and a change in temperature of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the apparatus 800 and other devices, either in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of apparatus 800 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the method provided by the above embodiments.
The embodiment of the application also provides a computer program product, which comprises: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (9)

1. A method for predicting a test advance workload rate for a software test, the method comprising:
obtaining an independent variable combination of a software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test;
inputting the independent variable combination of the software item to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and obtaining the duty ratio information of the test forward movement workload of the software item to be tested; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of sample software projects and real duty ratio information of test forward workload of the sample software projects;
Generating and displaying prompt information according to the duty ratio information of the test forward workload of the software item to be tested, wherein the prompt information is used for indicating the duty ratio information of the test forward workload of the software item to be tested;
determining a confidence interval according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and residual information obtained by processing the data of the plurality of sample software items;
determining the test forward workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested;
the method further comprises the steps of:
acquiring a plurality of sample software project data; wherein the plurality of sample software item data are assigned to a plurality of categories of sample software items; each sample software item data comprises independent variable combinations of the sample software item and real duty ratio information of test forward workload of the sample software item; the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the test forward movement workload;
for each independent variable, performing multiple linear regression processing on the independent variables of the plurality of sample software project data and other independent variables of the plurality of sample software project data to obtain a decision coefficient corresponding to each independent variable;
Removing the independent variable with the decision coefficient larger than a first preset threshold value from the plurality of sample software project data;
processing the independent variables of the plurality of sample software project data and the real duty ratio information of the test forward workload of the plurality of sample software project data aiming at each independent variable to obtain Pearson correlation coefficients corresponding to each independent variable;
removing independent variables with the Pearson correlation coefficient smaller than a second preset threshold value from the plurality of sample software project data;
and processing the initial model according to the plurality of sample software project data to obtain a preset multiple regression statistical model.
2. The method of claim 1, wherein processing the initial model according to a plurality of sample software project data to obtain the preset multiple regression statistical model comprises:
inputting the multiple sample software project data subjected to the elimination processing into an initial model for processing to obtain factor parameters corresponding to each independent variable and estimated duty ratio information of test forward-shift workload of each sample software project data;
determining a model residual error item according to the estimated duty ratio information of the test forward workload of each sample software item data and the real duty ratio information of the test forward workload of each sample software item data;
And establishing the preset multiple regression statistical model according to factor parameters corresponding to each independent variable.
3. The method of claim 2, further comprising, after processing the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model:
repeating the following steps until the preset multiple regression statistical model is determined to be an effective model:
constructing first statistics information according to estimated duty ratio information of test forward workload of each sample software item data, real duty ratio information of test forward workload of each sample software item data, the number of sample software item data and the number of types of independent variables;
if the value represented by the first statistic information is larger than the critical value of a first preset statistic, determining the preset multiple regression statistical model as an effective model;
if the value represented by the first statistics information is smaller than or equal to the critical value of the first preset statistics, determining the type of the replacement independent variable, and restarting the step of acquiring the data of a plurality of sample software items.
4. The method of claim 2, further comprising, after processing the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model:
Repeating the following steps until each effective factor parameter is obtained:
determining second statistic information corresponding to each independent variable according to respective variable combinations of the plurality of sample software project data, factor parameters corresponding to each independent variable in the preset multiple regression statistical model and model residual items of the preset multiple regression statistical model;
if the value represented by the second statistic information corresponding to each independent variable is larger than the critical value of the second preset statistic, the factor parameter corresponding to the independent variable is an effective factor parameter;
if the value represented by the second statistic information corresponding to any independent variable is smaller than or equal to the critical value of the second preset statistic, eliminating the independent variable corresponding to the minimum second statistic information, and restarting executing the step of processing the initial model according to the plurality of sample software project data to obtain the preset multiple regression statistical model.
5. The method of claim 1, wherein the argument is any one of: software project scale information, internal collaboration complexity, external collaboration complexity, software project review level, system importance of software projects, system relationships of software projects, system deployment scale information of software projects, and number of test advance participation phases of software projects.
6. A device for predicting a test advance workload rate for a software test, the device comprising:
the first acquisition unit is used for acquiring an independent variable combination of the software item to be tested, wherein the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the forward workload of the test;
the prediction unit is used for inputting the independent variable combination of the software item to be tested into a preset multiple regression statistical model, wherein the preset multiple regression statistical model is provided with factor parameters corresponding to each independent variable, and the duty ratio information of the test forward workload of the software item to be tested is obtained; the factor parameters in the preset multiple regression statistical model are obtained by adopting a plurality of sample software project data, and each sample software project data comprises independent variable combinations of sample software projects and real duty ratio information of test forward workload of the sample software projects;
the prompting unit is used for generating and displaying prompting information according to the duty ratio information of the test forward workload of the software item to be tested, and the prompting information is used for indicating the duty ratio information of the test forward workload of the software item to be tested;
The first determining module is used for determining a confidence interval according to the duty ratio information of the test forward workload of the software item to be tested, the preset confidence and residual information obtained by processing the data of the plurality of sample software items;
the second determining module is used for determining the test forward workload of the software item to be tested according to the confidence interval and the test total workload of the software item to be tested;
a second acquisition unit configured to acquire a plurality of sample software item data; wherein the plurality of sample software item data are assigned to a plurality of categories of sample software items; each sample software item data comprises independent variable combinations of the sample software item and real duty ratio information of test forward workload of the sample software item; the independent variable combination comprises at least one independent variable, and the independent variable is a factor influencing the test forward movement workload;
the first processing unit is used for carrying out multiple linear regression processing on the independent variables of the plurality of sample software project data and other independent variables of the plurality of sample software project data aiming at each independent variable to obtain a decision coefficient corresponding to each independent variable;
The first rejecting unit is used for rejecting the independent variable with the decision coefficient larger than a first preset threshold value from the plurality of sample software item data;
the second processing unit is used for processing the independent variables of the plurality of sample software project data and the real duty ratio information of the test forward-shift workload of the plurality of sample software project data aiming at each independent variable to obtain Pearson correlation coefficients corresponding to each independent variable;
the second rejecting unit is used for rejecting independent variables with the Pearson correlation coefficient smaller than a second preset threshold value from the plurality of sample software project data;
and the training unit is used for processing the initial model according to the plurality of sample software project data to obtain a preset multiple regression statistical model.
7. An electronic device, the electronic device comprising: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the method of any of claims 1-5.
8. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any of claims 1-5.
9. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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CN111506508A (en) * 2020-04-17 2020-08-07 北京百度网讯科技有限公司 Edge calculation test method, device, equipment and readable storage medium

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CN108629355A (en) * 2017-03-21 2018-10-09 北京京东尚科信息技术有限公司 Method and apparatus for generating workload information
CN111506508A (en) * 2020-04-17 2020-08-07 北京百度网讯科技有限公司 Edge calculation test method, device, equipment and readable storage medium

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