CN110865939B - Application program quality monitoring method, device, computer equipment and storage medium - Google Patents
Application program quality monitoring method, device, computer equipment and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 84
- 238000012544 monitoring process Methods 0.000 title claims abstract description 31
- 238000012360 testing method Methods 0.000 claims abstract description 261
- 238000011156 evaluation Methods 0.000 claims abstract description 40
- 238000012806 monitoring device Methods 0.000 claims abstract description 5
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- 238000004590 computer program Methods 0.000 claims description 20
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- G—PHYSICS
- 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
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G—PHYSICS
- 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
- G06F11/3692—Test management for test results analysis
Abstract
The application relates to an application program quality monitoring method, an application program quality monitoring device, computer equipment and a storage medium. Relates to the technical field of computers. The method comprises the following steps: firstly, acquiring a test case set, wherein the test case set comprises at least one test case; then, testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module; then, according to the test result, determining a target functional module in the application program, wherein the probability of the target functional module failure is larger than a preset probability threshold; and finally, evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program. The method can shorten the time for evaluating the quality of the application program.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for monitoring quality of an application program, a computer device, and a storage medium.
Background
With the development of computer technology, the development of application programs has become commonplace. In the development process of the application program, the quality of the application program needs to be monitored. Commonly, behavior driven development (English: BDD) technology can be utilized to realize quality monitoring of application programs.
In the related process of monitoring the quality of the application program by utilizing the BDD technology, a test case needs to be prepared in advance, and then the application program is tested by utilizing the test case, so that an execution result of the test case is obtained. And then analyzing the execution result manually, writing a test report, and finally evaluating the quality and risk of the application program according to the test report.
However, in the related process of monitoring the quality of an application program by using the BDD technology, the quality and risk of the application program can be known by manually analyzing the execution result and writing a test report. Therefore, in the case of a large number of test cases, there is a problem in that it takes a long time to evaluate the quality of the application.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an application quality monitoring method, apparatus, computer device, and storage medium that can shorten the duration.
In a first aspect, there is provided a method for monitoring quality of an application, the method comprising:
acquiring a test case set, wherein the test case set comprises at least one test case;
testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module;
determining a target functional module in the application program according to the test result, wherein the probability of the target functional module failure is greater than a preset probability threshold;
and evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program.
In one embodiment, the determining the target function module in the application program according to the test result includes:
obtaining the probability of faults of each functional module output by the membership function according to the test result by using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
In one embodiment, the membership function is constructed from a Bayesian classification function or a hidden Markov computational model.
In one embodiment, the evaluating the quality index corresponding to the target function module to obtain the evaluation information of the application program includes:
and inputting the numerical value of the quality index corresponding to the target functional module into a fuzzy inference model to obtain the evaluation information output by the fuzzy inference model.
In one embodiment, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
an unrepaired number for characterizing the number of defects that have not been repaired during the test;
the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In one embodiment, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In one embodiment, the obtaining the test case set includes:
and acquiring the test case and a label of the functional module corresponding to the test case, wherein the label is used for indicating the functional module to which the test case belongs.
In a second aspect, there is provided an application quality monitoring apparatus, the apparatus comprising:
the acquisition module is used for acquiring a test case set, wherein the test case set comprises at least one test case;
the test module is used for testing the application program by using the test cases in the test case set to obtain a test result, and the application program comprises at least one functional module;
the determining module is used for determining a target functional module in the application program according to the test result, wherein the probability of the occurrence of the fault of the target functional module is larger than a preset probability threshold;
and the evaluation module is used for evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program.
In one embodiment, the determining module is specifically configured to obtain, according to the test result, a probability of failure of each functional module output by the membership function by using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
In one embodiment, the membership function is constructed from a Bayesian classification function or a hidden Markov computational model.
In one embodiment, the evaluation module is specifically configured to input a numerical value of a quality index corresponding to the target functional module into the fuzzy inference model, so as to obtain the evaluation information output by the fuzzy inference model.
In one embodiment, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
an unrepaired number for characterizing the number of defects that have not been repaired during the test;
The secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In one embodiment, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In one embodiment, the acquiring module is specifically configured to acquire the test case and a tag of a functional module corresponding to the test case, where the tag is used to indicate the functional module to which the test case belongs.
In a third aspect, there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the first aspects when the computer program is executed.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of the first aspects described above.
The application program quality monitoring method, the device, the computer equipment and the storage medium are characterized in that firstly, a test case set is obtained, and the test case set comprises at least one test case; then, testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module; then, according to the test result, determining a target functional module in the application program, wherein the probability of the target functional module failure is larger than a preset probability threshold; and finally, evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program. Because the quality monitoring method of the application program provided by the application can evaluate the quality and risk of the application program without manually writing a test report, the quality monitoring method of the application program provided by the application shortens the time for evaluating the quality of the application program to a certain extent.
Drawings
FIG. 1 is an application scenario diagram of an application quality monitoring method in one embodiment;
FIG. 2 is a flow chart of an application quality monitoring method in one embodiment;
FIG. 3 is a flow diagram of determining a target function module in an application in one embodiment;
FIG. 4 is a block diagram of an application quality monitoring device in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
With the development of computer technology, the development of application programs has become commonplace. In the development process of the application program, the quality of the application program needs to be monitored. Commonly, behavior driven development (English: BDD) technology can be utilized to realize quality monitoring of application programs. The BDD provides a general, simple and structured description language, which can be English or other local languages, and can conveniently enable developers and ordinary people to smoothly communicate with demands on application programs.
In the related process of monitoring the quality of the application program by utilizing the BDD technology, a test case needs to be prepared in advance, wherein the test case refers to the description of a test task of a specific application program, and a test scheme, a method, a technology and a strategy are embodied. The content of the method comprises a test target, a test environment, input data, a test step, an expected result, a test script and the like, and finally a document is formed. Briefly, a test case is a set of test inputs, execution conditions, and expected results tailored for a particular goal to verify that the needs of a particular application are met. And then, testing the application program by using the test case to obtain an execution result of the test case. And then analyzing the execution result manually, writing a test report, and finally evaluating the quality and risk of the application program according to the test report.
However, in the related process of monitoring the quality of an application program by using the BDD technology, the quality and risk of the application program can be known by manually analyzing the execution result and writing a test report. Therefore, in the case of a large number of test cases, there is a problem in that it takes a long time to evaluate the quality of the application.
The application program quality monitoring method provided by the application can be applied to an application environment shown in figure 1. Wherein the terminal 101 communicates with the server 102 via a network connection. The server 102 may receive the test case set sent by the terminal 101, then the server 102 may test the application program by using the test case, then the server 102 may determine a target functional module (i.e. a functional module with a probability of failure exceeding a preset probability threshold) according to a test result obtained by the test, then the server 102 may evaluate a quality index generated by the target functional module in a test process to obtain evaluation information, and send the evaluation information to the terminal 102, and the terminal 102 may display the evaluation information, where the evaluation information may indicate quality and risk of the application program.
It should be noted that, in some possible implementations, the implementation environment related to the application quality monitoring method provided by the present application may include only the terminal 101 or only the server 102.
The terminal 101 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 102 may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
In the embodiment of the present application, as shown in fig. 2, an application quality monitoring method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
in step 201, a server obtains a test case set, where the test case set includes at least one test case.
The test case is a small amount of test data which is carefully designed for efficiently finding out the defects of the application program, and is the basis for the test of the application program.
In this step, the test cases can be divided into different categories according to the functional modules of the application program, and then the application program can be tested by selecting the corresponding test cases according to the requirements during actual testing. For example, the application program is divided into a user management function module, a role management function module, a right control function module, and the like according to the function. The test cases of user creation, user inquiry, user information maintenance, user account opening, user account unlocking and user association can be divided into the user management function modules. Then, in the actual test, the application program may be tested by selecting only the test cases corresponding to the user management function module.
Step 202, the server tests the application program by using the test cases in the test case set to obtain a test result.
The application includes at least one functional module.
In the step, when the application program is tested by using the test case, corresponding test results are obtained, and the test results have very important roles in analyzing the quality of the application program in subsequent operation.
Typically, a test pattern will contain at least one step, and an expected result. For example, there is a test case of a login application, which includes three steps of inputting an account number, inputting a password, and clicking a login button. The expected result is that the application will switch to the successful login interface if both the entered account number and password are correct and the login button is clicked correctly.
In the actual test, when the application program is tested by using the test case, two test results are obtained, and the login is successful or the login fails. If the login fails, then the specific case of the login failure can be analyzed. In particular, the specific steps in which problems occur can be analyzed to find out which problems exist in developing an application.
Step 203, the server determines the target function module in the application program according to the test result.
The probability of the target functional module failure is greater than a preset probability threshold.
In this step, specifically, the portion of the test result where the problem occurs may be emphasized to analyze, so as to infer the portion of the application program where the fault may occur. These potentially faulty portions are then monitored with emphasis in subsequent operations.
And 204, the server evaluates the quality index corresponding to the target functional module to obtain the evaluation information of the application program.
The assessment information is used to indicate the quality and risk of the application.
In this step, after the target functional module is determined, key monitoring needs to be performed for the target functional module. Specifically, quality index data generated in the testing process of the test case corresponding to the target functional module can be analyzed, so that quality and risk of the whole application program can be known. The quality and risk of an application can be judged according to whether the application is reliable, the average time the application can run normally, whether the application is easy to expand, the ratio of the number of lines of code annotation to the number of lines of code of the application, and whether the format of the application code annotation is uniform and clear.
In the application program quality monitoring method, firstly, a test case set is obtained, wherein the test case set comprises at least one test case; then, testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module; then, according to the test result, determining a target functional module in the application program, wherein the probability of the target functional module failure is larger than a preset probability threshold; and finally, evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program. Because the quality monitoring method of the application program provided by the application can evaluate the quality and risk of the application program without manually writing a test report, the quality monitoring method of the application program provided by the application shortens the time for evaluating the quality of the application program to a certain extent.
In an embodiment of the present application, please refer to fig. 3, a method for determining a target function module in an application program is provided, the method includes:
step 301, the server obtains the probability of failure of each functional module output by the membership function according to the test result by using the membership function.
Membership functions, also known as home functions or fuzzy primitive functions, are functions that may be used in fuzzy sets, indicating whether an element in a set belongs to a particular subset. The value of the indicator function of an element may be 0 or 1, while the membership function of an element may be a value between 0 and 1, indicating the "degree of realism" that the element belongs to a fuzzy set.
In this step, the probability of failure of each functional module may be determined by using the membership function, or it may be understood that the degree of reality that each functional module belongs to the failed module is determined by using the membership function.
Step 302, the server screens out functional modules with probability of being out of order from the probability of being out of order, wherein the probability of being out of order is greater than the preset probability threshold.
In this step, for example, the preset probability threshold is 0.7, and when the membership function predicts that the degree of reality that each functional module belongs to the fault module is 0.4, 0.5, and 0.8, respectively. Wherein 0.8 is greater than a preset probability threshold value of 0.7, then the functional module corresponding to 0.8 can be considered to have a fault.
In step 303, the server determines the functional module with the probability of possible failure greater than the preset probability threshold as the target functional module.
Based on the above steps, after the functional module corresponding to 0.8 is selected, at this time, the functional module corresponding to 0.8 may be considered to be faulty, and then the functional module corresponding to 0.8 is taken as the target functional module. In the subsequent monitoring, analysis can be focused on the quality index corresponding to the functional module. Thereby evaluating the quality and risk of the application.
In an embodiment of the present application, the membership function is constructed based on a Bayesian classification function or a hidden Markov calculation model.
The Bayesian classification is an irregular classification method, a Bayesian classification function is used for training a classified sample subset, learning and summarizing the classification function, and the classification of unclassified data is realized by using a classifier obtained through training. The application effect of the method is better than that of a neural network classification algorithm and a decision tree classification algorithm, and particularly when the data quantity to be classified is very large, the Bayesian classification method has high accuracy compared with other classification algorithms.
The hidden Markov model is a dynamic Bayesian network and is mainly used for modeling time sequence data.
In the embodiment of the application, the membership function is constructed by using a Bayesian classification function or a hidden Markov calculation model, so that the finally obtained membership function has better classification performance.
In an embodiment of the present application, there is provided a method of obtaining application evaluation information, the method including: and the server inputs the numerical value of the quality index corresponding to the target functional module into a fuzzy inference model to obtain the evaluation information output by the fuzzy inference model.
Fuzzy inference, a system that defines input, output and state variables on fuzzy sets, is a generalization of deterministic systems. The fuzzy system is from macroscopic view, grasps the fuzzy characteristic of human brain thinking, has the advantage of describing high-level knowledge, and can simulate comprehensive inference of human to solve the problem of fuzzy information processing which is difficult to be solved by the conventional mathematical method.
In the embodiment of the application, optionally, the values of the defect repair rate, the repaired number, the unrepaired number, the secondary failure rate and the test passing rate corresponding to the target functional module can be input into the fuzzy inference model. And then the fuzzy inference model performs fuzzy relation calculation and cluster analysis on the data, and on the basis, fuzzy mapping and comprehensive judgment are performed, so that the obtained result can be used for judging the quality and the risk of an application program.
In the embodiment of the application, the fuzzy reasoning model is used, so that the problem of nonlinearity can be solved well, for example, the problem of how to evaluate the quality of an application program in the application can be solved.
In the embodiment of the application, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
an unrepaired number for characterizing the number of defects that have not been repaired during the test;
the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In the embodiment of the application, the quality index can be used for quality evaluation and risk evaluation of the application program. The quality index mainly selects data of faults and defects of the test case in the test process. The quality and fault of the application program can be better estimated by using the data and the fuzzy inference model.
In an embodiment of the present application, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In the embodiment of the application, the quality fuzzy inference model can predict a specific value, and the value can be used for measuring the quality of the application program. The risk fuzzy inference model can predict the probability of the application program failure, and a user can judge whether the application program is reliable or not according to the quality value of the application program and the probability value of the application program failure.
For example, the values of the quality indexes may be obtained by taking percentages, drawing a radar chart according to specific percentage values, and calculating an area value of the obtained radar chart, where the area value represents a value obtained by fusing the values of the quality indexes together, and the value may be used as a criterion for evaluating the quality of the application program.
In the embodiment of the application, a quality fuzzy inference model and a risk fuzzy inference model are constructed on the basis of the fuzzy inference model. Quality information and risk information of the application program can be obtained at the same time, so that the application program can be more comprehensively evaluated by utilizing the two models.
In an embodiment of the present application, a method for acquiring a test case set is provided, where the method includes: the server acquires the test case and the label of the functional module corresponding to the test case, wherein the label is used for indicating the functional module to which the test case belongs.
In the embodiment of the application, an application program developer can divide the application program into different functional modules according to different functions in the application program. And then corresponding test cases are formulated for different functional modules, so that the specific conditions of the application program can be more comprehensively considered when the test cases are formulated, and the formulated test cases can be used for testing the application program more comprehensively.
Further, because each test case has its own corresponding functional module. Therefore, when the application program is tested by utilizing a certain test case, if the test fails, the fault cause can be rapidly positioned to the functional module corresponding to the test case. And when the membership function predicts the target functional module, the range of the quality index needed next can be clearly found out according to the corresponding relation between the functional module and the test case.
It should be understood that, although the steps in the flowcharts in fig. 2 to 3 are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of other steps or sub-steps or stages of other steps.
In an embodiment of the present application, as shown in fig. 4, there is provided an application quality monitoring apparatus 400, including: an acquisition module 401, a test module 402, a determination module 403, and an evaluation module 404, wherein:
an obtaining module 401, configured to obtain a test case set, where the test case set includes at least one test case;
a test module 402, configured to test an application program by using a test case in the test case set to obtain a test result, where the application program includes at least one functional module;
a determining module 403, configured to determine, according to the test result, a target functional module in the application program, where a probability of occurrence of the target functional module is greater than a preset probability threshold;
and the evaluation module 404 is configured to evaluate the quality index corresponding to the target function module to obtain evaluation information of the application program, where the evaluation information is used to indicate quality and risk of the application program.
In the embodiment of the present application, the determining module 403 is specifically configured to obtain, according to the test result, a probability of occurrence of a fault of each functional module output by the membership function by using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
And determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
In an embodiment of the present application, the membership function is constructed based on a Bayesian classification function or a hidden Markov calculation model.
In the embodiment of the present application, the evaluation module 404 is specifically configured to input the value of the quality index corresponding to the target functional module into the fuzzy inference model, so as to obtain the evaluation information output by the fuzzy inference model.
In the embodiment of the application, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
an unrepaired number for characterizing the number of defects that have not been repaired during the test;
the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In an embodiment of the present application, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In this embodiment of the present application, the obtaining module 401 is specifically configured to obtain the test case and a tag of a functional module corresponding to the test case, where the tag is used to indicate the functional module to which the test case belongs.
For specific limitations of the application quality monitoring device, reference may be made to the above limitation of the application quality monitoring method, and no further description is given here. The various modules in the application quality monitoring device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing application quality monitoring data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an application quality monitoring method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment of the present application, there is provided a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a test case set, wherein the test case set comprises at least one test case;
testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module;
determining a target functional module in the application program according to the test result, wherein the probability of the target functional module failure is greater than a preset probability threshold;
and evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program.
In an embodiment of the present application, the processor when executing the computer program further implements the following steps:
obtaining the probability of faults of each functional module output by the membership function according to the test result by using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
In an embodiment of the present application, the membership function is constructed based on a Bayesian classification function or a hidden Markov calculation model.
In an embodiment of the present application, the processor when executing the computer program further implements the following steps:
and inputting the numerical value of the quality index corresponding to the target functional module into a fuzzy inference model to obtain the evaluation information output by the fuzzy inference model.
In the embodiment of the application, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
An unrepaired number for characterizing the number of defects that have not been repaired during the test;
the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In an embodiment of the present application, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In an embodiment of the present application, the processor when executing the computer program further implements the following steps:
and acquiring the test case and a label of the functional module corresponding to the test case, wherein the label is used for indicating the functional module to which the test case belongs.
In an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a test case set, wherein the test case set comprises at least one test case;
Testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module;
determining a target functional module in the application program according to the test result, wherein the probability of the target functional module failure is greater than a preset probability threshold;
and evaluating the quality index corresponding to the target functional module to obtain evaluation information of the application program, wherein the evaluation information is used for indicating the quality and risk of the application program.
In an embodiment of the present application, the computer program when executed by the processor further implements the steps of:
obtaining the probability of faults of each functional module output by the membership function according to the test result by using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
In an embodiment of the present application, the membership function is constructed based on a Bayesian classification function or a hidden Markov calculation model.
In an embodiment of the present application, the computer program when executed by the processor further implements the steps of:
And inputting the numerical value of the quality index corresponding to the target functional module into a fuzzy inference model to obtain the evaluation information output by the fuzzy inference model.
In the embodiment of the application, the quality index comprises:
a defect repair rate that characterizes a ratio of the number of repaired defects to the number of total defects during the test;
a repaired number that characterizes a number of defects that have been repaired during the testing process;
an unrepaired number for characterizing the number of defects that have not been repaired during the test;
the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process;
and the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
In an embodiment of the present application, the fuzzy inference model includes: a quality fuzzy inference model for predicting a defect rate of the application program, and a risk fuzzy inference model for predicting a probability that the application program may fail.
In an embodiment of the present application, the computer program when executed by the processor further implements the steps of:
and acquiring the test case and a label of the functional module corresponding to the test case, wherein the label is used for indicating the functional module to which the test case belongs.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for monitoring the quality of an application, the method comprising:
acquiring a test case set, wherein the test case set comprises at least one test case;
testing an application program by using the test cases in the test case set to obtain a test result, wherein the application program comprises at least one functional module;
judging the probability of faults of each functional module by using a membership function according to the test result, and determining a target functional module in the application program, wherein the probability of faults of the target functional module is larger than a preset probability threshold;
Inputting the quality index corresponding to the target functional module into a fuzzy inference model to obtain evaluation information output by the fuzzy inference model, wherein the evaluation information is used for indicating the quality and risk of the application program; the fuzzy inference model comprises a quality fuzzy inference model and a risk fuzzy inference model, wherein the quality fuzzy inference model is used for estimating a numerical value, and the numerical value is used for measuring the quality of the application program; the risk fuzzy inference model is used for presuming the probability that the application program is likely to fail;
the quality fuzzy inference model is used for taking the percentage of each value of the quality index, drawing a radar chart according to the value of the percentage, and finally calculating the area value of the radar chart;
the quality index comprises: defect repair rate, number of repaired, number of unrepaired, secondary failure rate, test pass rate; the defect repair rate is used for representing the ratio of the number of repaired defects to the number of all defects in the test process; the repaired number is used for representing the number of defects which have been repaired in the test process; the unrepaired number is used for representing the number of defects which are not repaired in the test process; the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process; the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
2. The method according to claim 1, wherein determining the target function module in the application program by determining the probability of failure of each function module using a membership function according to the test result comprises:
obtaining the probability of faults of each functional module output by the membership function according to the test result by utilizing the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
3. The method according to claim 1 or 2, wherein the membership functions are constructed from bayesian classification functions or hidden markov calculation models.
4. A method according to claim 1 or 2, wherein the application program comprises a user management function module, a role management function module and a rights control function module.
5. The method according to claim 1 or 2, wherein the obtaining the set of test cases comprises:
And acquiring the test case and a label of the functional module corresponding to the test case, wherein the label is used for indicating the functional module to which the test case belongs.
6. An application quality monitoring device, the device comprising:
the system comprises an acquisition module, a test case generation module and a test case generation module, wherein the acquisition module is used for acquiring a test case set, and the test case set comprises at least one test case;
the test module is used for testing the application program by using the test cases in the test case set to obtain a test result, and the application program comprises at least one functional module;
the determining module is used for determining the probability of faults of each functional module by using a membership function according to the test result, and determining a target functional module in the application program, wherein the probability of faults of the target functional module is larger than a preset probability threshold;
the evaluation module is used for inputting the numerical value of the quality index corresponding to the target functional module into the fuzzy inference model to obtain evaluation information output by the fuzzy inference model, wherein the evaluation information is used for indicating the quality and risk of the application program; the fuzzy inference model comprises a quality fuzzy inference model and a risk fuzzy inference model, wherein the quality fuzzy inference model is used for estimating a numerical value, and the numerical value is used for measuring the quality of the application program; the risk fuzzy inference model is used for presuming the probability that the application program is likely to fail;
The method comprises the steps of taking the percentage of each value of the quality index, drawing a radar chart according to the percentage value, and finally calculating the area value of the radar chart;
the quality index comprises: defect repair rate, number of repaired, number of unrepaired, secondary failure rate, test pass rate; the defect repair rate is used for representing the ratio of the number of repaired defects to the number of all defects in the test process; the repaired number is used for representing the number of defects which have been repaired in the test process; the unrepaired number is used for representing the number of defects which are not repaired in the test process; the secondary failure rate is used for representing the ratio of the number of defects repaired more than once to the number of all defects in the test process; the test passing rate is used for representing the ratio of the number of the test cases which are normally operated to the number of all the test cases in the test process.
7. The apparatus of claim 6, wherein the obtaining module is further configured to obtain the test case and a tag of a functional module corresponding to the test case, where the tag is used to indicate the functional module to which the test case belongs.
8. The apparatus of claim 6, wherein the determining module is further configured to obtain, according to the test result, a probability of failure of each functional module output by the membership function using the membership function;
screening out functional modules with the probability of possible faults larger than the preset probability threshold value from the probability of possible faults of the functional modules;
and determining the functional module with the probability of possible faults larger than the preset probability threshold as the target functional module.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 5 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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