CN115239122A - Digital power grid software project tester recommendation method and device - Google Patents
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Abstract
The invention discloses a method and a device for recommending digital power grid software project testers, wherein the method comprises the following steps: determining the types of test tasks according to the test requirements of the software project, wherein the types of the test tasks comprise common test tasks and important test tasks; for a common test task, the capability of testers is effectively analyzed by analyzing the characteristics of a test system, and a group of reliable testers is recommended; for important test tasks, the tester is effectively recommended based on three dimensional characteristics of the test environment, the test capability and the field knowledge of the tester, and the tester is effectively recommended with the goals of maximizing the error detection probability of the tester, the relevance of the tester to the test tasks, the diversity of the personnel and minimizing the test cost. The invention combines the type of the task and the characteristics of the testers, recommends proper testers for the test task, improves the defect detection rate and shortens the task completion period.
Description
Technical Field
The invention relates to a method and a device for recommending digital power grid software project testers, which are applied to the field of power grid software testing.
Background
Currently, an important mark leading to the digital transformation of a power grid is the deep application of new technologies such as the internet, artificial intelligence, big data, the internet of things and the like based on a cloud platform. The construction of digital power grids will make future power grid production operations highly dependent on networking and informatization. With the deep development of power grid information construction and the deep application of information testing, the trend of diversification and complication of testing environment, testing type, testing problem and testing data is presented, the traditional testing mode for centrally managing testing projects and centrally constructing professional testing capability faces huge challenges, and the testing of some important software projects usually needs to consume nearly half of testing resources (manpower, equipment and the like), which is unacceptable.
Faced with the challenges that testing resources are difficult to meet the rapidly growing testing demands and testing methods are also difficult to respond quickly to new requirements of business changes, a new set of software testing tools has been developed to perform more and faster tests, but additional investment is required to develop new tools, and testing personnel with very skilled but scarce technologies are often required, and labor cost is also increased. Therefore, much of the expert work in grid informatization has focused on reducing the cost of test resources for test tasks. How to maximize the input gain of each tester based on the capability of the existing personnel has important significance on the construction of power grid informatization software projects.
Disclosure of Invention
The invention aims to: the invention aims to provide a method and a device for recommending digital power grid software project testers, which can be used for maximizing the input gain of each tester based on the capability of the existing personnel and effectively recommending reliable testers to complete testing tasks.
The technical scheme is as follows: in a first aspect, a method for recommending digital power grid software project testers comprises the following steps:
determining the types of test tasks according to the test requirements of the software project, wherein the types of the test tasks comprise common test tasks and important test tasks;
for a common test task, acquiring functions to be tested by analyzing the characteristics of a software system to be tested, extracting relevant data from a storage library, calculating the dependency relationship among the functions based on the extracted data to construct a software function dependency relationship tree, identifying the test parallelism of subsystem functions based on the dependency relationship, establishing a proper candidate ranking list based on a medal counter for a subsystem capable of performing parallel test through different test tasks, and giving a tester recommendation result of each subsystem by comprehensively calculating the ranking and contribution of each candidate to the system test;
for important test tasks, software, hardware and environmental attributes influencing test results are obtained by analyzing the context of the running of the test tasks, and test environmental characteristics are constructed; acquiring the testing capability characteristics of the tester based on the historical testing result of the tester; establishing the domain knowledge characteristics of the testers based on the domain test experience obtained by executing the power grid test task on the testers; based on the test environment characteristics, the test capability characteristics and the field knowledge characteristics, an objective model is established and solved with the aim of maximizing the error detection probability of the testers, the correlation with the test tasks, the diversity of the testers and minimizing the test cost, and the matched testers are recommended based on the solution result.
Preferably, the test requirements include test quantity and completion time, and when the corresponding test requirement of a software project is not higher than a preset threshold, the test task is taken as a common test task, otherwise, the test task is taken as an important test task.
In a second aspect, a recommendation device for digital grid software project testers includes:
the testing task type determining module is used for determining the type of a testing task according to the testing requirements of the software project, wherein the testing task type comprises a common testing task and an important testing task;
the system comprises a common test task recommending module, a function to be tested and a database, wherein for a common test task, the function to be tested is obtained by analyzing the characteristics of a software system to be tested, relevant data is extracted from the database, the dependency relationship among the functions is calculated based on the extracted data to construct a software function dependency relationship tree, the test parallelism of subsystem functions is identified based on the dependency relationship, the subsystems are subsystems which can be tested in parallel through different test tasks, a ranking list of suitable candidates based on a medal counter is established, and the ranking and contribution of each candidate to the system test are comprehensively calculated to give a recommended result of the tester of each subsystem;
the important test task recommending module is used for analyzing the context of the running of the test task to obtain software, hardware and environmental attributes influencing the test result for the important test task and constructing test environmental characteristics; acquiring the testing capability characteristics of the tester based on the historical testing result of the tester; establishing field knowledge characteristics of the testers based on field test experience obtained by executing the power grid test tasks on the testers; based on the test environment characteristics, the test capability characteristics and the field knowledge characteristics, an objective model is established and solved with the aim of maximizing the error detection probability of the testers, the correlation with the test tasks, the diversity of the testers and minimizing the test cost, and the matched testers are recommended based on the solution result.
In a third aspect, the present invention also provides a computer device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors, implement the steps of the digital grid software project tester recommendation method as described above.
In a fourth aspect, the present invention further provides a computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the digital grid software project tester recommendation method as described above.
Has the advantages that: the present invention maximizes the input gain of each tester for different testing tasks. Aiming at common test tasks, the knowledge coverage rate of the test personnel on the test tasks is checked by utilizing the existing project team, and the group of developers is displayed to select the developer with the highest combined coverage rate. Aiming at large important test tasks, an optimization model which aims at maximizing the error detection probability of testers, the correlation with the test tasks, the diversity of the testers and the minimum test cost is established by extracting test environment, test capability and field knowledge to effectively recommend the testers, improve the defect detection rate and shorten the task completion period. The invention combines the type of the task and the characteristics of the tester team to recommend proper testers for the test task, detects more software defects by fewer personnel, and effectively improves the overall testing efficiency.
Drawings
FIG. 1 is a flow chart of a digital power grid software project tester recommendation method of the present invention;
FIG. 2 is a flow chart of a method for human recommendation for generic test tasks in accordance with the present invention;
FIG. 3 is a flow chart of a method for personnel recommendation for important test tasks in accordance with the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
Referring to fig. 1, the digital power grid software project tester recommendation method of the invention comprises the following steps:
and S10, determining the types of test tasks according to the test requirements of the software project, wherein the types of the test tasks comprise common test tasks and important test tasks.
The test service management platform obtains the test task requirements, and when system software developed by a manufacturer needs to be tested, the relevant information of the system to be tested is submitted on the test service management platform, wherein the relevant information comprises the requirements of the test task. In the embodiment of the invention, the test requirements comprise the test quantity and the completion time, and the urgency degree of a project can be calculated according to the test quantity and the completion time, generally speaking, when the corresponding test requirement of a software project and/or the urgency degree derived based on the test requirement is not higher than a preset threshold value, the test task is taken as a common test task, otherwise, the test task is taken as an important test task.
According to the embodiment of the invention, the measured quantity mainly comprises a project type, a project level, a project emergency degree and the number of cases to be tested, wherein the project type mainly comprises a safety development project, a test development project, a tool development project and other development projects; the project level comprises general projects, a first-class key projects and a second-class key projects; item urgency = (deadline for submitting test report-general test time)/average test period, where the general test time is matched test time normally according to difficulty of the function point, and if the general function performance test is 30min/h, more time spent with higher difficulty may be spent, and the average test period is an average value of the test periods for the item types; the number of cases to be tested is the number of cases to be tested given according to the total number of the requirements.
According to the embodiment of the invention, in the process of testing various project types (namely, security class development, test class development, tool class development and other development projects), the classification of testing tasks mainly comprises the following steps:
aiming at a general project, the emergency degree of the project is more than 1, and meanwhile, the number of cases to be tested is less than 1/2 of the same type of project, the test is divided into common test tasks:
aiming at important projects, the emergency degree of the projects is less than 1, and meanwhile, the number of cases to be tested is higher than 1/2 of the same type of projects, so that the important test tasks are provided.
By classifying the test tasks, testers suitable for corresponding task characteristics can be recommended for tasks of different classes in a targeted manner, and the improvement of the overall test efficiency is facilitated.
And S20, in the face of a common test task, recommending the most appropriate tester in the aspect of capability knowledge coverage of the tester to construct the most appropriate team in the cooperation of the test task by effectively analyzing the system change history, the function dependency relationship and the like in the multi-version iteration of the test.
The method comprises the steps of obtaining functions to be tested by analyzing characteristics of a software system to be tested, extracting test data submitted together from a storage library, firstly applying for testing resources, then calculating the dependency relationship among the functions to construct a software function dependency relationship tree, identifying the testing parallelism of subsystem functions based on the dependency relationship, establishing a proper candidate ranking list based on a medal counter for a subsystem capable of performing parallel testing through different testing tasks, and giving a tester recommendation result of each subsystem by comprehensively calculating the ranking and contribution of each candidate to system testing.
Referring to fig. 2, in one example, a method for personnel recommendation for a generic test task includes:
step S21: in the face of a certain system to be tested of a common test task, relevant data are extracted from a storage library by submitting test requirements, wherein the data mainly comprise a requirement specification of the system, a user operation manual, an environment configuration table, a function dependency relationship analysis table, a factory test report and the like; and then comprehensively considering the extracted related data, taking the test function of the software to be detected as a root node, analyzing by using a software function dependency relationship file, searching all dependencies of the software function, wherein the dependencies comprise third-party software and function calls in a software package to form a software dependency relationship tree, and leaf nodes of the tree represent the dependencies of the software function and corresponding versions and states of function completion, wherein the versions mainly refer to the system change history number in the software iteration process, and the states mainly refer to the test passing state and the difficulty level. Meanwhile, the leaf nodes of the tree are continuously traversed through the data, the dependence of the dependent software function is searched, and the dependence is recorded as the child node of the current node. And repeating the steps to find out the software function dependency relationship of all the nodes and the child nodes until the leaf nodes do not depend on other functions any more, thereby forming a complete software function dependency relationship tree taking the software function module to be tested as the root node.
Step S22: and viewing the constructed software dependency relationship tree, dividing subsystems which can be separately tested based on test requirements, wherein for one subsystem, the test version of the original test system is 1.0, the change history number is 0, and the difficulty coefficient is 0. Checking the system version and the state in version iteration of a retested subsystem, and if the change history number is more than 1 and the test state of the retested subsystem fails, adding 1 to the difficulty level of the retested subsystem;
step S23: the method comprises the steps that a rank list of suitable candidates based on a medal counter is obtained, medals are counted through a testing system which is used for testing the members in a testing group, one-time testing is conducted through 1 complete subsystem, the medal count of a tester is increased by 1, sorting of the medal count is conducted based on the tester, matching of the tester is conducted through difficulty level mapping of different subsystems, and the tester recommendation of each subsystem is given.
And S30, aiming at important test tasks, establishing a multi-objective optimization model to effectively recommend testers based on three dimensional characteristics of the test environment, the test capability and the field knowledge of the power grid testers.
Aiming at a large test task, a large number of small tasks are executed by utilizing a large number of test resources in a mass test mode, so that the cost can be reduced, and the task can be effectively ensured to be completed within a specified time. However, in the distribution process, how to optimize the personnel involved in the test needs to comprehensively consider environment, capability and background skills.
Referring to FIG. 3, in one example, a method for personnel recommendation for important test tasks includes:
and S31, extracting the test environment characteristics.
The characteristics of the test environment mainly refer to that the test task is operated in a specific context, which can affect the components of the test result, and mainly include the attributes of hardware equipment model, software operating system, network environment and the like owned by the test staff. These properties may be shared across different test tasks, as they may be needed across different projects to reproduce the defects of the test application.
The model of the hardware equipment is mainly considered on the basis of the equipment memory and the equipment storage capacity, and the testing efficiency can be effectively improved under the condition of higher basic performance of the equipment; the system requirements of a software operating system, which are primarily directed to a test environment, should remain consistent with the system requirements of a subsequent production application, and operating system inconsistencies may result in some security-based, performance-testing inaccuracies. The network environment is a guarantee for realizing communication, and indexes such as throughput (I/O), round trip delay (RTT), and the like have an important influence on the interaction performance between devices, and it is needless to say that a good network environment will improve the test efficiency, so this factor must be considered.
For the hardware device model and the software operating system, the tester uploads the basic information after registering the test service management and control platform, so that the basic attribute characteristics can be obtained. For a network environment, this may be obtained based on a test of communication between devices. For example, the data size of a test-related file transmitted between devices in an actual test period is counted and divided by the uploading time to obtain the network I/O capability, and/or the RTT required for the transmission from a sending end to a receiving end is obtained as the network environment attribute.
And S32, extracting the testing capability features.
The test capability is characterized by the capability to be abstracted from the historical test results of the tester. Although competency is a relatively abstract concept, it can also be reflected in a number of aspects of the correlation results, and the present invention uses attributes to describe a tester's competency, including primarily the number of participating items, the number of test reports submitted, the number of error reports submitted, the percentage of error reports submitted, and the degree to which the tester repeats the error reports. A test report is a test result submitted by a test worker after a test task is completed and contains a report ID, a worker ID (i.e., the person who submitted the report), a task ID (i.e., which task was performed), a description of the manner in which the test was performed and what happened during the test, error tags, and repetition tags. In particular, the tags are assigned by the project manager to indicate whether a report contains an "error" (i.e., an error tag), and whether the report is a "duplicate" (i.e., a duplicate tag) of other reports.
And the quantized digital statistics are used as a capability value vector of the tester. In one embodiment, the percentage of error reports submitted and the extent to which the tester repeats the error reports is calculated by the following formula:
the percentage of error reports submitted by the tester = number of error reports submitted by the tester/number of test reports submitted.
The extent to which the tester repeats the error report = the repeat index of the tester/the number of error reports submitted by the tester.
And S33, extracting the domain knowledge characteristics.
The characteristic of the domain knowledge is the experience of the power grid domain information system test obtained by a tester through executing the test task of the power grid. The applications being tested are usually from different business domains of the grid, which requires testers with knowledge of a specific business domain to better explore the functions related to testing the business. Familiar domain knowledge of a tester is represented by using "descriptive terms" extracted from the tester's historical submission report, and represented as a vector.
In one example, the domain knowledge characterization descriptive terms are obtained as follows:
a list of descriptive terms is first constructed from all tasks in the training dataset, followed by word segmentation and elimination of stop words to reduce noise. The terms are sorted according to the number of reports (i.e., document frequency df) in which one term appears, and then a certain percentage (e.g., 5%) of the terms with the lowest document frequency are filtered out. At a threshold of 5%, the recommendation effect may reach a good and stable value. Secondly, words are extracted from the historical submission report of the tester, and the words are mapped with the descriptive term list, namely, are searched against the descriptive term list, so that descriptive terms representing the knowledge in the field of the tester are obtained.
And S34, establishing a multi-objective optimization model.
In the face of large-scale power grid test tasks, the test tasks are required to be completed within a specified time when entrusted to test personnel, and a group of test personnel are required to be effectively recommended to complete the test tasks together. Not all testers are equally adept at finding defects. The improper testers may miss the defects or report repeated defects, the testers depend on each other, and a group of proper testers is recommended for one test task according to different levels of the different testers facing defect finding, so that more software defects can be detected by fewer workers, and the overall testing efficiency is effectively improved.
Since the goal recommended by software testers is to help test out as many bugs as possible with fewer testers, the testing task is completed. First, the tester with the greatest probability of defect detection should be recommended because it can potentially improve defect detection performance. Next, the testing task for the grid information system is basically user-driven and complex, so the correlation between the workers and the task should be considered, and the testing personnel with maximized professional knowledge related to the testing task should be found, because the personnel have more business background knowledge to increase the possibility of defect detection. Second, selecting a group of people with different characteristics will help detect more defects and reduce duplicate reports for different areas where different people may explore the application being tested. In addition, test costs should be taken into account.
Therefore, in the embodiment of the present invention, it is aimed to maximize the defect detection probability of the tester, the correlation with the test task, the diversity of the worker, and minimize the test cost.
According to the embodiment of the invention, aiming at the goal of maximizing the defect detection probability, a machine learning model is established by determining the characteristics to learn the defect detection probability of each tester. The extraction of features has an important influence on the identification of the model, and according to the exploration and verification of the invention, the defect detection features comprise: (1) It is a common consensus in the industry that the abilities of a tester are closely related to the defect detection probability, and therefore, all ability-related attributes of a tester are considered as features in a machine learning model. (2) The working experience of the test person has a great influence on the execution of the test work, and the experience is formed by the accumulation of the history, so that the past experience of the test person is better simulated by further considering the factors related to the time in the invention. Extracting the working conditions of past 2 weeks, 1 month and 2 months through the capability attributes. Thus, the original one attribute can yield four features in the machine learning model. (3) Another time-dependent feature, the time interval between the test person's last submission to the test task release, in days, is used in the machine learning model. The longer this time interval, the less likely the tester will be involved in the task.
For the above features, the model is machine-learned by using logistic regression. Based on a logistic regression model trained on the training data set, given a task in the test data set, the probability of defect detection of the model for all candidate testers can be obtained. For a group of candidate test workers, the sum is considered as the probability of defect detection for a test task by adding their probability of defect detection over a given test task.
The present invention uses a logistic regression algorithm for classification. According to the above description, the defect detection feature includes 5 feature indexes and is represented by a point in a 5-dimensional space. For each set of data x input by sigmoid function (i) Can be mapped to a number between 0 and 1. If the function value is larger than 0.5, the value is judged to belong to 1, otherwise, the value belongs to 0. Moreover, the function needs a parameter to be determined, and the parameter can be accurately predicted for data in a training set by utilizing sample training.
According to embodiments of the present invention, for a relevance target, a measure of the relevance between candidate testers and a test task is required. The correlation is expressed by using the similarity between the field knowledge of the tester and the test task. The invention calculates based on cosine similarity between descriptive terms of the knowledge of the tester in the field and descriptive terms of the test task requirements. A larger similarity value indicates that the domain knowledge of the tester is more closely related to the testing task.
Given a specific large test task, in order to obtain the correlation of a group of candidate testers, the domain knowledge of all selected testers is combined into a unified vector, and then the cosine similarity vector of the testers is calculated based on the requirements of the test task. The method specifically comprises the following steps:
(1) Acquiring a test task requirement through a test service management platform, and constructing a descriptive term list based on the requirement of a test task contained in the test task requirement;
(2) Calculating cosine similarity between descriptive terms of field knowledge of candidate testers and descriptive terms of requirements of the test tasks; taking out a plurality of keywords from descriptive terms of field knowledge of candidate testing personnel and descriptive terms of testing task requirements respectively, combining the keywords into a set, calculating the word frequency of the descriptive terms of the candidate testing personnel on the words in the set, generating respective word frequency vectors, and calculating to obtain the cosine similarity of the two vectors;
(3) And sorting is carried out based on the values of the calculated cosine similarity, and the larger the value is, the more similar the value is, and the stronger the correlation is.
In addition to maximizing tester relevance in accordance with embodiments of the present invention in the search for testers familiar with testing tasks, the nature of software testing requires different testers to help explore various parts of the application and reduce duplicate test reports. Thus, maximizing tester diversity aims to find testers with different backgrounds. Although these two goals seem to conflict with each other, the goal of the present invention is to strike a balance between relevance maximization and diversity maximization through a multi-objective optimization framework. To explore attribute diversity, it was measured using a count-based approach and calculated how many different attribute values appeared in the selected test person set.
As mentioned above, the tester has three features: testing environmental, capability and domain knowledge. For the capability dimension, it is not reasonable to consider diversity. Thus, diversity is calculated based on the other two dimensions. Specifically, for a test environment, how many different operating systems, network environments, etc. are included in a group of testers is calculated. For domain knowledge, by counting how many different terms appear in the worker's domain knowledge. And calculating diversity according to the magnitude difference of the attributes of the test environment and the field knowledge through the attributes of the test environment and the field knowledge, and finally obtaining a final diversity value by using the weight parameter.
In particular, in an embodiment of the invention, the diversity of the test persons =0.5 +0.5 of the test environment + the diversity of the domain knowledge attributes. The diversity of the test environments depends on the difference of the test environments equipped by a group of testers, and the difference is a group of different environments as long as the difference occurs in the aspects of an operating system, a network environment and the like. Test environment diversity = number of different environments/total set of tester environments. Multiplicity of domain knowledge attributes = how many different terms/total number of domain knowledge terms appear in the domain knowledge. It should be understood that the weighting of 0.5 in the test person diversity calculation formula is merely an example and may be other weighting in other embodiments. By trying different weights, the invention can obtain relatively good and stable performance when the weight value is 0.5.
In accordance with embodiments of the present invention, cost is an inevitable goal in recommending testers for system testing tasks, with a view to minimizing testing costs. The most important cost in the test is the reward to the tester. Assuming that all participating testers are equally rewarded for an expedited task, a selected set of testers costs may be weighed. Depending on the test requirements, the corresponding number of testers multiplied by the reward may result in a test cost.
The final objective function is expressed as:
f(x)＝[f 1 (x),f 2 (x),f 3 (x),f 4 (x)]
wherein f is 1 (x)、f 2 (x)、f 3 (x) And f 4 (x) The above-mentioned partial objective functions are expressed separately, i.e., the objectives of maximizing the probability of defect detection of the tester, the correlation with the test task, the diversity of the worker, and minimizing the test cost are achieved.
And S35, solving the multi-target model to obtain recommended testers.
Multi-objective optimization has difficulty in obtaining optimal results for all objectives simultaneously. More test personnel may be required to do the job, such as to maximize the probability of false detection, thus sacrificing minimal testing costs. The present invention seeks a pareto frontier solution. The objective was optimized by using the non-dominated sorting genetic algorithm-II (NSGA-II). In the tester recommendation scenario, the pareto frontier represents the best trade-off between the four goals identified by NSGA-II. The tester may then examine the pareto frontier to find the best compromise between tester choices that balance the probability of false detection, correlation, diversity, and cost of testing, or the tester choice that maximizes the penalty of three of the targets for the remaining targets.
The implementation of the multi-objective optimization mainly comprises 4 steps of decoding, initialization, genetic operator and fitness function.
1) And (3) decoding: by encoding each test worker as a binary variable. If test operation is selected, the value is 1; otherwise, the value is zero. The solution is represented as a vector of binary variables whose length is equal to the number of candidate test workers. The solution space for the recommended problem for the test worker is the set of all possible combinations, regardless of whether each test worker is selected.
2) And (5) initializing. The initial population is randomly initialized, i.e., K (K is the size of the initial population) solutions are randomly selected among all possible solutions, and K is set to 200.
3) And (4) genetic operator. For the evolution of binary coding of solutions, the next generation is generated using single-point crossing, mutation, with standard operators. With the league as the selection operator, where both solutions are randomly selected, the fitter of both will live in the next population.
4) A fitness function. Since the goal of the present invention is to optimize the four considered goals, each candidate solution is evaluated by the described objective function. For error detection probability, correlation and diversity, the larger these values, the faster the solution converges, while the test cost benefits from the smaller values.
Based on the same inventive concept as the method embodiment, the invention also provides a digital power grid software project tester recommending device, which comprises:
the test task type determining module is used for determining the type of a test task according to the test requirement of the software project, wherein the test task type comprises a common test task and an important test task;
the system comprises a common test task recommending module, a test task recommending module and a data processing module, wherein for a common test task, functions to be tested are obtained by analyzing the characteristics of a software system to be tested, relevant data are extracted from a storage library, a software function dependency relationship tree is constructed by calculating the dependency relationship among the functions based on the extracted data, the test parallelism of subsystem functions is identified based on the dependency relationship, the subsystems are subsystems capable of performing parallel test through different test tasks, a proper candidate ranking list based on a medal counter is established, and the recommendation result of testers of each subsystem is given by comprehensively calculating the ranking and contribution of each candidate to the system test;
the important test task recommending module is used for analyzing the context of the running of the test task to obtain software, hardware and environmental attributes influencing the test result for the important test task and constructing test environmental characteristics; acquiring the testing capability characteristics of the tester based on the historical testing result of the tester; establishing the domain knowledge characteristics of the testers based on the domain test experience obtained by executing the power grid test task on the testers; based on the test environment characteristics, the test capability characteristics and the field knowledge characteristics, an objective model is built and solved with the aim of maximizing the error detection probability of the testers, the correlation with the test tasks, the diversity of the testers and minimizing the test cost, and the matched testers are recommended based on the solution result.
In an embodiment of the present invention, the test requirements include a test quantity and a completion time, where the test quantity mainly includes a project type, a project level, a project emergency degree, and a number of cases to be tested, and the test task type determining module specifically includes:
the project emergency degree determining unit is used for calculating the emergency degree of the project according to the test amount and the completion time, wherein the project type mainly comprises a safety development project, a test development project, a tool development project and other development projects; the project level comprises a general project, a first-class key project and a second-class key project; item urgency = (deadline for submitting test report-general test time)/average test period, wherein the general test time is the matched test time normally according to the difficulty of the functional point, and the average test period is the average value of the test period for the item type; the number of cases to be tested is the number of cases to be tested given according to the total number of the demands;
and the test task classification unit is used for classifying the test tasks, and when the corresponding test requirement of a software project and/or the emergency degree derived based on the test requirement is not higher than a preset threshold value, the test tasks are used as common test tasks, otherwise, the test tasks are used as important test tasks. According to the embodiment of the invention, the classification of the test tasks for various project types is as follows: aiming at a general project, the emergency degree of the project is more than 1, and the number of cases to be tested is less than 1/2 of the same type of project, the test is divided into common test tasks: aiming at important projects, the emergency degree of the projects is less than 1, and meanwhile, the number of cases to be tested is higher than 1/2 of the same type of projects, so that the important test tasks are provided.
According to the embodiment of the invention, the common test task recommending module comprises:
the data extraction unit is used for extracting relevant data from the storage library based on test requirements, wherein the relevant data comprises a requirement specification of a system, a user operation manual, an environment configuration table, a function dependency relationship analysis table and a factory test report; calculating the dependency relationship among all functions based on the extracted data to construct a software function dependency relationship tree, which comprises the following steps: taking a test function of software to be detected as a root node, analyzing by using a software function dependency relationship file, and searching all dependencies of the software function, wherein the dependencies comprise third-party software and function calls in a software package to form a software dependency relationship tree, leaf nodes of the tree represent the dependencies of the software function and corresponding versions and states of function completion, the versions mainly comprise system change history numbers in a software iteration process, and the states are test passing states and difficulty levels;
the data analysis unit is used for dividing subsystems which can be separately tested based on test requirements according to the constructed software dependency relationship tree, aiming at one subsystem, the tested version of the original test system is 1.0, the change history number is 0, the difficulty coefficient is 0, aiming at the retested subsystem, the system version and the state in version iteration of the retested subsystem are checked, and if the change history number is more than 1 and the test state of the retested subsystem is not passed, the difficulty level is increased by 1;
and the personnel recommending unit is used for establishing a ranking list of suitable candidates based on the medal counter, performing medal counting on a test system in which the testing of the personnel in the test group is completed, performing one-time testing through 1 complete subsystem, adding 1 to the medal counting of the tester, performing one sequencing of the medal counting based on the tester, performing matching of the tester through the difficulty level mapping of different subsystems, and giving the recommendation of the tester of each subsystem.
In an embodiment of the present invention, the important test task recommending module includes: the system comprises a test environment feature extraction unit, a test capability feature extraction unit, a domain knowledge feature extraction unit, a multi-objective optimization model construction unit and a solving unit; the test environment feature extraction unit is used for acquiring software, hardware and environment attributes influencing a test result by analyzing the context of the running of the test task and constructing test environment features; the test capability feature extraction unit is used for acquiring the test capability features of the testers based on the historical test results of the testers; the domain knowledge feature extraction unit is used for establishing domain knowledge features of testers based on domain test experiences obtained by executing power grid test tasks on the testers; the multi-objective optimization model building unit is used for building an objective model with the objectives of maximizing the error detection probability of testers, the correlation with test tasks, the diversity of the testers and minimizing the test cost on the basis of the test environment characteristics, the test capability characteristics and the field knowledge characteristics; and the solving unit is used for solving the target model and recommending matched testers based on the solving result.
According to the embodiment of the invention, the test environment characteristics comprise the model of hardware equipment, a software operating system and a network environment owned by a test worker;
the testing capability characteristics of the tester include the number of participating items, the number of submitted test reports, the number of submitted error reports, the percentage of error reports submitted, the degree to which the tester repeats the error reports, wherein,
the percentage of error reports submitted by the tester = number of error reports submitted by the tester/number of test reports submitted;
the extent to which the tester repeats the error report = the repeat index of the tester/the number of error reports submitted by the tester.
In an embodiment of the present invention, the domain knowledge characteristics of the tester include descriptive terms of domain knowledge of the tester, and the obtaining method includes: constructing a descriptive term list according to all tasks in a training data set, segmenting words and deleting stop words, sequencing entries according to the number of reports of the occurrence of an entry, filtering out entries with a certain proportion of the lowest document frequency, extracting words from historical submission reports of a tester, and mapping the words and the descriptive term list to obtain descriptive terms representing the knowledge in the tester field.
In an embodiment of the present invention, the calculation of the correlation between the tester and the test task includes:
(1) Acquiring a test task requirement through a test service management platform, and constructing a descriptive term list based on the requirement of a test task contained in the test task requirement;
(2) Calculating cosine similarity between descriptive terms of field knowledge of candidate testers and descriptive terms of requirements of the test tasks; taking out a plurality of key words from descriptive terms of field knowledge of candidate testing personnel and descriptive terms of testing task requirements respectively, combining the key words into a set, calculating word frequencies of the two descriptive terms for words in the set, generating respective word frequency vectors, and calculating to obtain cosine similarity of the two vectors;
(3) And sorting is carried out based on the values of the calculated cosine similarity, and the larger the value is, the more similar the value is, and the stronger the correlation is.
In the embodiment of the invention, the method for acquiring the error detection probability of the tester comprises the following steps:
(1) Extracting defect detection related features, including: all capability-related attributes of the tester; extracting the working conditions of the testers in the past 2 weeks, 1 month and 2 months based on the capability attributes of the testers, and the time interval from the last submission of the testers to the release of the test tasks;
(2) For the extracted features, using a logistic regression model trained on a training data set to give defect detection probabilities for all candidate testers for a task in a given test data set;
(3) For a set of candidate test workers, the sum is considered as the defect detection probability of the test task by adding the defect detection probabilities thereof on the given test task.
In an embodiment of the invention, the diversity of the test person = α + the diversity of the test environment + the diversity of the domain knowledge attributes. Wherein, α and β are corresponding weights respectively, the diversity of the testing environment depends on the difference of the testing environment equipped by a group of testing personnel, and the different environment is a group of different environments as long as the difference occurs in the aspects of operating system, network environment, etc. Test environment diversity = number of different environments/total set of tester environments. Multiplicity of domain knowledge attributes = how many different terms/total number of domain knowledge terms appear in the domain knowledge. By trying different weights, the invention can obtain relatively good and stable performance when the weight value is 0.5.
In an embodiment of the invention, the solution unit optimizes the objective by using a non-dominated sorting genetic algorithm-II (NSGA-II). In the tester recommendation scenario, the pareto frontier represents the best trade-off between the four goals identified by NSGA-II. The tester may examine the pareto frontier to find the best compromise between tester choices that balance the probability of false detection, correlation, diversity, and cost of testing, or the tester choice that maximizes the three goals to penalize the remaining goals.
The present invention also provides a computer apparatus comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors, implement the steps of the digital grid software project tester recommendation method as described above.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for recommending digital grid software project testers as described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A recommendation method for a tester of a digital power grid software project is characterized by comprising the following steps:
determining the types of test tasks according to the test requirements of the software project, wherein the types of the test tasks comprise common test tasks and important test tasks;
for a common test task, acquiring functions to be tested by analyzing the characteristics of a software system to be tested, extracting relevant data from a storage library, calculating the dependency relationship among the functions based on the extracted data to construct a software function dependency relationship tree, identifying the test parallelism of subsystem functions based on the dependency relationship, establishing a proper candidate ranking list based on a medal counter for a subsystem capable of performing parallel test through different test tasks, and giving a tester recommendation result of each subsystem by comprehensively calculating the ranking and contribution of each candidate to the system test;
for important test tasks, software, hardware and environmental attributes influencing test results are obtained by analyzing the context of the running of the test tasks, and test environmental characteristics are constructed; acquiring the testing capability characteristics of the tester based on the historical testing result of the tester; establishing field knowledge characteristics of the testers based on field test experience obtained by executing the power grid test tasks on the testers; based on the test environment characteristics, the test capability characteristics and the field knowledge characteristics, an objective model is built and solved with the aim of maximizing the error detection probability of the testers, the correlation with the test tasks, the diversity of the testers and minimizing the test cost, and the matched testers are recommended based on the solution result.
2. The method of claim 1, wherein the testing requirements include a testing amount and a completion time, and the urgency of the item is derived from the testing amount and the completion time, and the corresponding testing requirements and/or the urgency derived based on the testing requirements of the software item are used as a general testing task when not higher than a preset threshold, and are used as an important testing task otherwise.
3. The method of claim 1, wherein the relevant data extracted from the repository includes a requirement specification of the system, a user operation manual, an environment configuration table, a function dependency analysis table, and a factory test report;
the method for constructing the software function dependency relationship tree based on the extracted data and the dependency relationship among the functions comprises the following steps: the method comprises the steps of taking a test function of software to be detected as a root node, analyzing by utilizing a software function dependency relation file, and searching all dependencies of the software function, wherein the dependencies comprise third-party software and function calls in a software package to form a software dependency relation tree, leaf nodes of the tree represent the dependencies of the software function and corresponding versions and states of function completion, the versions mainly comprise system change history numbers in a software iteration process, and the states are test passing states and difficulty levels.
4. The method of claim 1, wherein the test environment characteristics include hardware device model, software operating system, and network environment owned by a test worker;
the testing capability characteristics of the tester include the number of participating items, the number of test reports submitted, the number of error reports submitted, the percentage of error reports submitted, and the degree to which the tester repeats the error reports, wherein,
the percentage of error reports submitted by the tester = number of error reports submitted by the tester/number of test reports submitted;
the degree to which the tester repeats the error report = the repeat index of the tester/the number of error reports submitted by the tester.
5. The method of claim 1, wherein the domain knowledge characteristics of the test person comprise descriptive terms of the domain knowledge of the test person, and the obtaining method comprises: the method comprises the steps of constructing a descriptive term list according to all tasks in a training data set, segmenting words, deleting stop words, sequencing the entries according to the number of reports of the occurrence of one entry, filtering out the entries with a certain proportion of the lowest document frequency, extracting words from historical submission reports of testers, and mapping the words and the descriptive term list to obtain descriptive terms representing the field knowledge of the testers.
6. The method of claim 5, wherein the calculation of the relevance of the test person to the test task comprises:
(1) Acquiring a test task requirement through a test service management platform, and constructing a descriptive term list based on the requirement of a test task contained in the test task requirement;
(2) Calculating cosine similarity between descriptive terms of field knowledge of candidate testers and descriptive terms of requirements of the test tasks; taking out a plurality of keywords from descriptive terms of field knowledge of candidate testing personnel and descriptive terms of testing task requirements respectively, combining the keywords into a set, calculating the word frequency of the descriptive terms of the candidate testing personnel on the words in the set, generating respective word frequency vectors, and calculating to obtain the cosine similarity of the two vectors;
(3) And sorting is carried out based on the calculated cosine similarity, wherein the larger the value is, the more similar the value is, and the stronger the correlation is.
7. The method of claim 1, wherein the method for obtaining the false detection probability of the tester comprises:
(1) Extracting defect detection related features, comprising: all capability-related attributes of the tester; extracting the working conditions of the testers in the past 2 weeks, 1 month and 2 months based on the capability attributes of the testers, and the time interval from the last submission of the testers to the release of the test tasks;
(2) For the extracted features, using a logistic regression model trained on a training data set to give defect detection probabilities for all candidate testers for a task in a given test data set;
(3) For a group of candidate test workers, the sum is considered as the probability of defect detection for a test task by adding their probability of defect detection over a given test task.
8. A digital power grid software project tester recommendation device is characterized by comprising: .
The testing task type determining module is used for determining the type of a testing task according to the testing requirements of the software project, wherein the testing task type comprises a common testing task and an important testing task;
the system comprises a common test task recommending module, a test task recommending module and a data processing module, wherein for a common test task, functions to be tested are obtained by analyzing the characteristics of a software system to be tested, relevant data are extracted from a storage library, a software function dependency relationship tree is constructed by calculating the dependency relationship among the functions based on the extracted data, the test parallelism of subsystem functions is identified based on the dependency relationship, the subsystems are subsystems capable of performing parallel test through different test tasks, a proper candidate ranking list based on a medal counter is established, and the recommendation result of testers of each subsystem is given by comprehensively calculating the ranking and contribution of each candidate to the system test;
the important test task recommending module is used for analyzing the context of the running of the test task to obtain software, hardware and environmental attributes influencing the test result for the important test task and constructing test environmental characteristics; acquiring the testing capability characteristics of the tester based on the historical testing result of the tester; establishing field knowledge characteristics of the testers based on field test experience obtained by executing the power grid test tasks on the testers; based on the test environment characteristics, the test capability characteristics and the field knowledge characteristics, an objective model is built and solved with the aim of maximizing the error detection probability of the testers, the correlation with the test tasks, the diversity of the testers and minimizing the test cost, and the matched testers are recommended based on the solution result.
9. A computer device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs when executed by the processors implement the steps of the digital power grid software project tester recommendation method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the digital grid software project tester recommendation method according to any one of the claims 1-7.
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