System for testing credential application software based on artificial intelligence

Abstract

The invention discloses a credential application software testing system and method based on artificial intelligence, and relates to the technical field of computer science and software engineering. The invention discloses a credential application software testing system based on artificial intelligence. The system comprises a demand analysis module, a test case generation module, a self-adaptive test execution module, an intelligent analysis module and a continuous integration and feedback module. The demand analysis module is responsible for analyzing a software demand document; the test case generation module generates a test case according to the demand analysis result; the self-adaptive test execution module is used for testing the generated test case by using a dynamic priority adjustment method; the intelligent analysis module analyzes the test result and generates a test report; and the continuous integration and feedback module is used for integrating the whole test system into a process of artificial intelligence software development, so that intelligent feedback and continuous optimization of the test process are realized.

Description

Technical Field

[0001] The present invention relates to the field of computer science and software engineering technology, and specifically to an artificial intelligence-based trusted computing application software testing system, and more particularly to a dynamic priority adjustment method in the system. Background Art

[0002] Xinchuang (innovation in information technology applications) is an important development strategy in my country's information technology field, and software testing is a key link in ensuring the quality of Xinchuang application software. Traditional software testing methods often rely on manual testing and static priority allocation. During manual testing, testers cannot fully cover all test scenarios, and static priority allocation methods are often based on fixed rules and experience, lack flexibility and cannot adapt to complex and changing test environments. In addition, traditional software testing also has problems in resource consumption. Manual testing and static priority allocation require a lot of manpower, material resources and time, which is a huge burden for teams with limited resources.

[0003] Therefore, exploring and introducing more efficient and intelligent software testing methods has become an important research direction in the current information field. Summary of the invention

[0004] The purpose of the present invention is to provide an artificial intelligence-based information technology application software testing system and a dynamic priority adjustment method, which can dynamically adjust the priority of tasks according to real-time feedback of test tasks and system status during the execution of automated tests to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an artificial intelligence-based trust innovation application software testing system, the system comprising a demand analysis module, a test case generation module, an adaptive test execution module, an intelligent analysis module, and a continuous integration and feedback module;

[0006] The requirements analysis module is used to parse software requirements documents, extract test requirements, and generate test plans; the test case generation module can build software usage scenarios according to the test plans and automatically generate test cases covering different scenarios; the adaptive test execution module is integrated with the test tool to automatically test the generated test cases and record the test results; the intelligent analysis module analyzes the collected test results, including defect identification, resource consumption, complexity, result evaluation and data analysis, and generates a test report; the continuous integration and feedback module integrates the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback and continuous testing.

[0007] The requirements analysis module, test case generation module, adaptive test execution module, intelligent analysis module and continuous integration and feedback module together constitute the system architecture of the test system. Among them, dynamic priority adjustment is installed in the adaptive test execution module as a core technology, which can adjust the priority of the task according to the real-time feedback of the test task and the system status, reduce the cost of manual intervention, and improve the test efficiency.

[0008] According to the above technical solution, the demand analysis module includes a natural language processing unit, a demand modeling unit, a test plan generation unit and a version control unit;

[0009] The natural language processing unit: uses NLP technology to parse the software requirement document and extract key information, such as functional requirements, performance requirements and security requirements. The requirement modeling unit: constructs a requirement model based on the extracted information, such as a use case diagram, architecture diagram and activity diagram, to better understand and form test requirements. The test plan generation unit: automatically generates a test plan based on the requirement model constructed by the modeling unit, including a test plan, test objectives and test strategy. The version control unit: performs version control on the requirement document and test plan to ensure that the test plan is consistent with the latest requirements.

[0010] According to the above technical solution, the test case generation module includes a scenario modeling unit and a test case generation unit;

[0011] The scenario modeling unit is used to build a software usage scenario model. It uses code analysis tools and an integrated development environment to build a scenario usage model based on the test requirements provided by the requirements analysis to ensure that the test cases cover different user operation processes and abnormal situations. The use case generation unit is a model built based on the scenario modeling unit, and automatically generates test cases according to the test plan generated by the requirements analysis module.

[0012] According to the above technical solution, the adaptive test execution module includes a test tool integration unit and an adaptive test integration unit;

[0013] The test tool integration unit is integrated with mainstream test tools to automatically generate test scripts according to the test environment and software usage scenarios to achieve automated testing; the adaptive test integration unit is used to automatically execute test cases in multiple environments, dynamically adjust the priority of test tasks according to the influence of various factors in the test project, and achieve more efficient task deployment and resource allocation;

[0014] The adjustment process includes the following steps:

[0015] Data collection and processing: Collect relevant data during the execution of test tasks; analyze and process the collected data to extract key characteristic parameters.

[0016] Build a priority calculation model: Build a priority calculation model. The task calculation formula of this model needs to consider five factors: complexity, importance, execution progress, resource consumption, and error rate of the test task. The following is the formula for calculating the dynamic priority of the test task:

[0017] P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T

[0018] (t) / T} δ +W5·{C(t) / C} ε

[0019] P(t) is the dynamic priority of the test task at time t.

[0020] I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient; the calculation formula of I(t) is as follows:

[0021] I(t)=I·[1+f(t)]·k

[0022] Among them, f(t) is the data corresponding to the factor at time t, which reflects the special attributes of the test task; k is an adjustment factor used to amplify or reduce the impact of f(t) on I(t) under the influence of different factors. R(t) is the resource consumption coefficient of the test task at time t, and R is a baseline resource consumption coefficient; different data need to be substituted according to the needs of the test project, and the calculation formula of R(t) is as follows:

[0023] R(t)=R·[1+g(t)]·k

[0024] Among them, g(t) is the factor at time t, which reflects the dynamic changes in the resource consumption of the test task; k is an adjustment factor used to amplify or reduce the impact of g(t) on R(t) under the influence of different factors. E(t) is the error rate coefficient of the test task at time t, and E is a baseline error rate coefficient; the calculation formula is as follows:

[0025] E(t)=D(t) / D 总 ·E

[0026] Where D(t) is the number of defects found at time t, D 总 It is the number of defects found during the entire testing process. When the actual number of defects increases or the expected total number of defects decreases, E(t) will increase, indicating an increase in the error rate.

[0027] T(t) is the execution progress coefficient of the test task at time t, and T is the maximum completion progress of the test task; the calculation formula is as follows:

[0028] T(t)=G(t) / G 总

[0029] Where G(t) is the number of test cases completed at time (t), G 总 is the total number of test cases in the test task; this formula means that at any time t, the completion progress of the test task is the number of completed test cases divided by the total number of test cases;

[0030] C(t) is the risk coefficient of the test task at time (t), taking into account the potential impact of the task on system stability. C is a baseline risk coefficient. According to the calculation formula of P(t) above, the calculation of the risk coefficient C(t) needs to consider the various situations mentioned above, including the urgency, stability, resource consumption, test error rate and task execution progress of the task. Taking stability as an example, the calculation formula of the risk coefficient C(t) is as follows:

[0031] C(t)=C·[1+k·[S(t)-S]]

[0032] Where S(t) represents the system stability data at time t; S represents the indicator value of the system in a stable state, which can be set by the project team by analyzing historical data to represent the stability baseline value; k is the adjustment factor, which determines the impact of stability changes on the risk factor.

[0033] The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1.

[0034] The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curves of various factors. The above exponents can be derived based on historical data analysis to optimize the sensitivity and accuracy of priority adjustment. The least squares method is used in linear regression models, and the maximum likelihood estimation method is used in nonlinear regression models. In more complex models, the gradient descent method can also be used to optimize parameters and apply formulas to obtain results to optimize the sensitivity and accuracy of priority adjustment.

[0035] According to the above technical solution, the intelligent analysis module includes a result preprocessing unit, a defect identification unit, a data analysis unit and a report generation unit;

[0036] The result preprocessing unit is used to preprocess the test results and classify the stable operation results and defect results; the defect identification unit is used to analyze the test results with artificial intelligence algorithms and identify potential software defects and abnormal data behaviors; the data analysis unit is used to perform intelligent analysis of the test results and provide defect severity classification and recommended repair measures; the report generation unit generates test reports in multiple formats based on the analysis results.

[0037] According to the above technical solution, the integration and feedback module includes a continuous integration process unit, a feedback mechanism unit and a test result visualization unit;

[0038] The continuous integration process unit refers to integrating the test system into the interface process to achieve automated construction, testing and deployment; the feedback mechanism unit refers to providing feedback to the development team based on the test results and intelligent analysis results; the test result visualization unit refers to displaying the test results and system status through dashboards and visualization tools to facilitate the development team to understand the project test status.

[0039] The purpose of the present invention is to provide an artificial intelligence-based information technology application software testing method and a dynamic priority adjustment method, which can dynamically adjust the priority of tasks according to real-time feedback of test tasks and system status during the execution of automated testing to solve the problems raised in the above-mentioned background technology.

[0040] To achieve the above purpose, the present invention provides the following technical solution: a method for testing ICT application software based on artificial intelligence, the method steps comprising the following contents:

[0041] S1. Parse the software requirement document, extract the test requirements, and generate the test plan and version control based on these requirements;

[0042] S2, build software usage scenarios based on the previously generated test plan, and automatically generate test cases covering these scenarios;

[0043] S3, integrate the generated test cases with the test tool, automatically execute the test cases, and record the test results;

[0044] S4. Analyze the collected test results, including but not limited to defect identification, resource consumption assessment, complexity analysis, result assessment and data analysis, and generate a test report based on the analysis results;

[0045] S5. Integrate the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback of test results and continuous testing process, and display test results and system status.

[0046] The software requirement analysis, extraction of test requirements, and generation of a test plan based on these test requirements include the following steps: parsing the software requirements described in natural language; extracting key information and test points from the requirement document; automatically generating a test plan based on the requirement model constructed by the modeling unit, the plan including a test plan, test objectives and test strategy, and implementing version control on the requirement document and the test plan to ensure that the test plan is always consistent with the latest requirements;

[0047] The method of constructing a usage scenario model according to a test plan and automatically generating test cases covering these scenarios includes the following steps: using a code analysis tool and an integrated development environment to construct a software usage scenario model according to the test requirements provided by the requirements analysis, and automatically generating test cases based on the scenario model constructed by scenario modeling and the test plan generated by the requirements analysis. The generated test cases will be used in the subsequent test execution process to ensure that the quality and performance of the software meet the predetermined requirements.

[0048] The test case is integrated with the test tool, and the test case is automatically executed and the test result is generated, which includes the following steps: the test tool integration unit is integrated with the mainstream test tool to prepare the environment for automated testing; according to the test environment and software usage scenario, the test tool integration unit automatically generates corresponding test scripts; the adaptive test integration unit is started to automatically execute the generated test cases in multiple test environments; during the test execution process, the adaptive test integration unit dynamically adjusts the priority of the test task according to various factors in the test project; by adjusting the priority, more efficient task deployment and resource allocation are achieved to optimize the test process and improve the test efficiency;

[0049] The dynamic priority adjustment process includes the following steps:

[0050] S1.1 Data collection and processing: Collect relevant data during the execution of the test task; analyze and process the collected data to extract key characteristic parameters.

[0051] S1.2 Construct a priority calculation model: Construct a priority calculation model. The task calculation formula of this model needs to consider five factors: complexity, importance, execution progress, resource consumption, and error rate of the test task. The following is the formula for calculating the dynamic priority of the test task:

[0052] P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T

[0053] (t) / T} δ +W5·{C(t) / C} ε

[0054] P(t) is the dynamic priority of the test task at time t;

[0055] I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient; the calculation formula of I(t) is as follows:

[0056] I(t)=I·[1+f(t)]·k

[0057] Where f(t) is the data corresponding to the factor at time t, and k is an adjustment factor;

[0058] R(t) is the resource consumption coefficient of the test task at time (t), and R is a baseline resource consumption coefficient; the calculation formula of R(t) is as follows:

[0059] R(t)=R·[1+g(t)]·k

[0060] Where g(t) is the factor at time t, and k is an adjustment factor;

[0061] E(t) is the error rate coefficient of the test task at time (t), and E is a baseline error rate coefficient;

[0062] The calculation formula is as follows:

[0063] E(t)=D(t) / D 总 ·E

[0064] Where D(t) is the number of defects found at time t, D 总 is the number of defects found during the entire testing process;

[0065] T(t) is the execution progress coefficient of the test task at time (t), and T is the maximum completion progress of the test task; the calculation formula is as follows:

[0066] T(t)=G(t) / G 总

[0067] Where G(t) is the number of test cases completed at time (t), G 总 is the total number of test cases for the test task;

[0068] C(t) is the risk factor of the test task at time (t), taking into account the potential impact of the task on system stability. C is a baseline risk factor. The calculation formula of C(t) is as follows:

[0069] C(t)=C·[1+k·[S(t)-S]]

[0070] Where S(t) represents the system stability data at time t, S represents the index value of the system in a stable state, and k is the adjustment factor;

[0071] The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1;

[0072] The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curve of each factor.

[0073] The test results are analyzed and a test report is generated based on the test results, including the following steps: preliminary processing of the test results to classify the test results into two categories: stable operation results and defect results; using an artificial intelligence algorithm to analyze the test results to identify potential software defects and abnormal data behavior; intelligently analyzing the identified test results, grading the severity of the defects, and making corresponding repair suggestions; generating a test report containing defect information and repair suggestions based on the results of preprocessing, identification and analysis, and ensuring that the test report can be output in multiple formats to meet different reporting requirements and reading habits.

[0074] The described steps of integrating the test system into the continuous integration / continuous deployment process of artificial intelligence software development, realizing intelligent feedback of test results and continuous progress of the test process, and displaying test results and system status include the following steps: integrating the test system into the interface process, configuring the process of automated building, testing and deployment, and ensuring that the test can be automatically executed after each code submission; after the execution is completed, the continuous integration process unit collects the test results and passes the results to the feedback mechanism unit; based on the test results and the results of the intelligent analysis, the necessary information is sorted out, and feedback is provided to the development team so as to keep abreast of software quality and performance issues; using dashboards and visualization tools, the test results and system status are converted into graphical displays, and intuitive charts and reports are generated; by viewing the charts and reports provided by the test result visualization unit, the test status and system status of the project can be quickly understood.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] Improve test efficiency: Dynamic priority adjustment technology can automatically adjust the priority of tasks according to the real-time feedback of test tasks and system status, which means that the most critical and vulnerable parts will be tested first. This priority adjustment can reduce unnecessary testing time and ensure that the testing of key functions receives sufficient attention.

[0077] Optimize resource allocation: By dynamically adjusting the priority of test tasks, the system can more effectively utilize resources such as CPU, memory, and tester time. Resources will be allocated to those test tasks that can most affect product quality, thereby improving resource utilization efficiency.

[0078] Real-time feedback and intelligent decision-making: Real-time data collected during test execution enables the system to respond quickly and adjust test priorities to cope with sudden changes in system status; artificial intelligence algorithms are used to analyze test results and system status to provide a scientific basis for dynamic priority adjustment.

[0079] Automated process: Automation of dynamic prioritization reduces manual intervention and the possibility of human error while increasing the degree of automation in the testing process.

[0080] Flexibility and adaptability: As projects progress and system status changes, dynamic prioritization technology can flexibly adapt to these changes, ensuring that testing efforts are always aligned with the current needs of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a system architecture diagram of the artificial intelligence-based ICT application software testing system of the present invention;

[0082] Figure 2 The present invention is a test flow chart of the test method of the information innovation application software based on artificial intelligence; DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0084] Example: Figure 1 As shown, the present invention provides a technical solution, a trust innovation application software testing system based on artificial intelligence, the system includes a demand analysis module, a test case generation module, an adaptive test execution module, an intelligent analysis module, and a continuous integration and feedback module;

[0085] The requirements analysis module is used to parse software requirements documents, extract test requirements, and generate test plans; the test case generation module can build software usage scenarios according to the test plans and automatically generate test cases covering different scenarios; the adaptive test execution module is integrated with the test tool to automatically test the generated test cases and record the test results; the intelligent analysis module analyzes the collected test results, including defect identification, resource consumption, complexity, result evaluation and data analysis, and generates a test report; the continuous integration and feedback module integrates the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback and continuous testing.

[0086] The requirement analysis module further includes a natural language processing unit, a requirement extraction unit, a test plan generation unit and a version control unit; the natural language processing unit is used to parse software requirements described in natural language; the requirement extraction unit is used to extract key information and test points from the requirement document; the test plan generation unit automatically generates a test plan according to the requirement model constructed by the modeling unit, including a test plan, test objectives and test strategies; the version control unit performs version control on the requirement document and the test plan to ensure that the test plan is consistent with the latest requirements.

[0087] Suppose we are testing a new function for an online shopping website, the user evaluation system; first we need the requirements analysis module to analyze the function of this project test and get the following test requirements document:

[0088] Project name: User evaluation system testing;

[0089] Project goal: Ensure the correctness, performance and security of user evaluation functions;

[0090] Test plan: Functional testing: ensure that all functions are executed according to the required specifications; Performance testing: evaluate the performance of the system under high load; Security testing: verify the security of the system under various attack attempts; Compatibility testing: ensure that the system runs normally on different browsers and devices.

[0091] The test case generation module further includes: a scenario modeling unit and a case generation unit; the scenario modeling unit is used to construct a software usage scenario model; the case generation unit is a model constructed based on the scenario modeling unit, and is used to automatically generate test cases.

[0092] After getting the test plan, the test case generation module is needed to build a test scenario usage model, generate use case modeling scenarios for submitting and viewing evaluations, use the automated testing framework to test the user evaluation function, and connect to the test system database to store, query, modify and maintain the test cases according to the provided test cases. According to the documents provided by the requirements analysis module, the generated test cases are as follows:

[0093] Functional test: Submit evaluation case; Input: user ID, product ID, rating (1-5 stars), evaluation content; View evaluation case: Input: user ID, product ID;

[0094] Performance test: Concurrent submission of evaluation use cases: Data: 100 users submit evaluations at the same time; Query evaluation: Data: 1000 concurrent query requests;

[0095] Security test: SQL injection: Data: Input specially constructed SQL injection statements; Cross-site scripting attack (XSS): Data: Input specially constructed XSS attack codes;

[0096] Compatibility testing: Browsing compatibility: data: Chrome, Firefox, Safari, Edge; Device compatibility: data: computer desktop, tablet, mobile phone;

[0097] The adaptive test execution module further includes: a test tool integration unit and an adaptive test integration unit; the test tool integration unit is used to automatically generate or assist in generating test scripts of automated test tools in different test tasks; the adaptive test integration unit is used to automatically execute test cases in multiple environments and adjust the priority of tasks according to different situations during the test process;

[0098] According to the test case results generated by the above test case generation module, the test phase of the adaptive execution module can be carried out. During the test process, the module will adjust the dynamic priority of the test case according to the test requirements. During the adjustment process, the importance, resource consumption, error rate, execution progress and risk of the test case need to be considered. The dynamic priority adjustment is calculated by the following formula:

[0099] P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T

[0100] (t) / T} δ +W5·{C(t) / C} ε

[0101] P(t) is the dynamic priority of the test task at time t.

[0102] I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient;

[0103] In this formula, the complexity, importance, execution progress, resource consumption, and error rate of the test task are considered to affect the results of the test task. The calculation formula of I(t) is as follows:

[0104] I(t)=I·[1+f(t)]·k

[0105] Where f(t) is the factor at time t, which reflects the special properties of the test task; k is an adjustment factor used to amplify or reduce the impact of f(t) on I(t) under the influence of different factors.

[0106] R(t) is the resource consumption coefficient of the test task at time t. R is a benchmark resource consumption coefficient. Different data needs to be substituted according to the requirements of the test project. The calculation formula of R(t) is as follows:

[0107] R(t)=R·[1+g(t)]·k

[0108] Among them, g(t) is the factor at time t, which reflects the dynamic changes in the resource consumption of the test task; k is an adjustment factor used to amplify or reduce the impact of g(t) on R(t) under the influence of different factors. E(t) is the error rate coefficient of the test task at time t, and E is a baseline error rate coefficient; considering the defect discovery of the test task at a specific time, and the relationship with the baseline error rate coefficient E to calculate E(t), the calculation formula is as follows:

[0109] E(t)=D(t) / D 总 ·E

[0110] Where D(t) is the number of defects found at time t, D 总 It is the number of defects found during the entire testing process. When the actual number of defects increases or the expected total number of defects decreases, E(t) will increase, indicating an increase in the error rate.

[0111] T(t) is the execution progress coefficient of the test task at time t, and T is the maximum completion progress of the test task. Based on the completion of the test task, the execution progress coefficient T(t) of the test task is the ratio of the number of completed test cases to the total number of test cases, and the calculation formula is as follows:

[0112] T(t)=G(t) / G 总

[0113] Where G(t) is the number of test cases completed at time t, G 总 is the total number of test cases for the test task; this formula indicates that at any time t, the completion progress of the test task is the number of completed test cases divided by the total number of test cases. Assuming that the project team wants to standardize the progress coefficient to the interval [0,1], and assuming that the maximum completion progress of the project is 1 (indicating 100% completion), when all cases are completed, T(t) will be equal to 1. C(t) is the risk coefficient of the test task at time (t), taking into account the potential impact of the task on system stability, C is a baseline risk coefficient; according to the calculation formula of P(t) above, the calculation of the risk coefficient C(t) needs to consider the various situations mentioned above, including the urgency, stability, resource consumption, test error rate and task execution progress of the task. Taking stability as an example, the calculation formula of the risk coefficient C(t) is as follows:

[0114] C(t)=C·[1+k·[S(t)-S]]

[0115] Among them, S(t) represents a system stability index at time t, which is assumed to be the failure rate or system operation time; S represents the index value of the system in a stable state, which can be set by the project team by analyzing historical data, representing the stability baseline value; k is the adjustment factor, which determines the degree of influence of stability changes on the risk factor. The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1.

[0116] The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curves of various factors. The above exponents can be obtained based on historical data analysis to optimize the sensitivity and accuracy of priority adjustment. The least squares method is used in linear regression models, and the maximum likelihood estimation method is used in nonlinear regression models. In more complex models, the gradient descent method can also be used to optimize the parameters and apply the formula to obtain the results to optimize the sensitivity and accuracy of priority adjustment. The following is the calculation of the dynamic priority adjustment of the submitted evaluation case, assuming that the priority adjustment index value is 1 (a result lower than 1 means that the priority is low and no adjustment is required, and vice versa):

[0117] According to the above calculation formula, we must first determine the importance coefficient I(t), and set an initial importance data of I=0.8 for the test item (indicating that the importance coefficient of the test task is 0.8 without any special attribute influence); factor f(t) at time t: set f(t)=0.2, which means that at the current moment, due to some special attributes (such as the launch of new functions), the importance of the test task has increased by 20%; adjustment factor k: set k=1.5, which means that we decide to amplify the influence of f(t) so that the influence of special attributes on I(t) is more significant; according to the given calculation formula I(t)=I·[1+f(t)]·k, substitute the above data to calculate:

[0118] I(t)=0.8·[1+0.2]·1.5, I(t)=1.44

[0119] The calculation results show that, considering the special attributes and adjustment factors, the importance coefficient I(t) of the test task has increased from the initial 0.8 to 1.44. This means that due to the special attributes and adjustment strategies at the current moment, the importance of the test task has increased, and a higher priority may be required to ensure its test quality and progress.

[0120] The next step is to calculate the resource consumption coefficient. The initial resource consumption coefficient R: R = 0.5 (indicates that the coefficient of test task resource consumption is 0.5 without any special attribute influence); factor g(t) at time t: set g(t) = 0.1, which means that at the current moment, due to some reasons (assuming that the system load increases, temporary activities), the test task resource consumption has increased by 10%; adjustment factor k: set k = 0.9, in order to decide to reduce the impact of g(t), because the increase in resource consumption may not affect the overall resource consumption as significantly as other factors. Calculate the resource consumption coefficient R(t), according to the given calculation formula: R(t) = R · [1 + g(t)] · k, substitute the above data to calculate:

[0121] R(t)=0.5·[1+0.1]·0.9, R(t)=0.495

[0122] The calculation results show that, considering the dynamic changes in resource consumption and the adjustment factors, the resource consumption coefficient R(t) of the test task has been reduced from the initial 0.5 to 0.495. This means that due to the special properties of the current moment and our adjustment strategy, resource consumption has actually been slightly reduced. This calculation result can be further substituted into the calculation of the dynamic priority P(t) to determine the priority of the test task.

[0123] Then calculate the error rate coefficient, the initial error rate coefficient E: E = 0.05 (indicates that the error rate of the test task is 5% without any special attribute influence); the number of defects found at time t D(t): set D(t) = 20, the total number of defects found during the entire test process Dtotal: set Dtotal = 100; calculate the error rate E(t), according to the given calculation formula E(t) = D(t) / Dtotal·E, substitute the above data to calculate:

[0124] E(t)=(20 / 100)·0.05,E(t)=0.01

[0125] The calculation results show that considering the number of defects found at time t and the total number of defects in the entire test process, the error rate of the test task E(t) has been reduced from the initial 5% to 1%. This means that the test quality at the current moment is better, the number of defects found is smaller, and the error rate has decreased relative to the entire test process;

[0126] Continue to calculate the execution progress coefficient, the number of completed test cases G(t) at time t: set G(t) = 150, the total number of test cases Gtotal of the test task: set Gtotal = 300; calculate the completion progress T(t) of the test task; according to the given calculation formula T(t) = G(t) / Gtotal, substitute the above data to calculate:

[0127] T(t)=150 / 300, T(t)=0.5

[0128] The calculation results show that at time t, the completion progress T(t) of the test task is 50%. This means that half of the test cases have been completed;

[0129] Finally, the risk coefficient is calculated, the initial risk coefficient C: C = 0.3 (indicates that the risk coefficient of the test task is 0.3 without any special attribute influence), the system stability index S(t) at time t: S(t) = 0.8 (here it is assumed that the system stability index is a value between 0 and 1, where 1 represents the best stability), the system stability index value S in the stable state: S = 0.9 (represents the stability index value of the system in the stable state); adjustment factor k: assume k = 0.1 (represents the degree of influence of each unit change of the system stability index on the risk coefficient); calculate the risk coefficient C(t), according to the given calculation formula C(t) = C [1 + k [S(t) - S]], substitute the above data to calculate:

[0130] C(t)=C·[1+k·[S(t)-S]], C(t)=0.297

[0131] The calculation results show that considering the change in the system stability index, the risk factor C(t) of the test task has decreased from the initial 0.3 to 0.297. This means that the risk factor has decreased slightly because the system stability index is slightly lower than the stable baseline value. This may indicate that the stability of the system has improved, or the change is not significant, so the impact on the overall risk is small;

[0132] According to the above calculation results, since the weights and indexes cannot be determined according to specific projects, these values ​​are assumed to be 1, and the calculation results of each part above are substituted into the formula:

[0133] P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T(t) / T} δ +W5·{C(t) / C} ε

[0134] We obtain P(t) = 3.227, which indicates that the priority of the task submitted for evaluation is relatively high because it exceeds 1.

[0135] The intelligent analysis module further includes a result preprocessing unit, a defect identification unit, a data analysis unit and a report generation unit; the result preprocessing unit refers to preprocessing the test results; the defect identification unit refers to using an artificial intelligence algorithm to analyze the test results and identify potential software defects and abnormal data behaviors; the data analysis unit refers to performing intelligent analysis on the test results, providing defect severity classification and recommended repair measures; the report generation unit generates test reports in multiple formats based on the analysis results.

[0136] After getting the test results, enter the intelligent analysis module, first collect the test results: collect the execution results of all test cases, including success, failure, and blocking status; secondly, perform data mining: use data mining technology to analyze the test results and identify patterns, trends, and anomalies; finally, perform problem diagnosis: conduct in-depth analysis of failed test cases to determine the reasons for failure, such as functional errors, performance issues, or security vulnerabilities;

[0137] When the above test process is completed, the test results of the test case will be obtained. Based on the above content, the intelligent analysis module will generate a test report:

[0138] Functional testing:

[0139] Submit evaluation use case: Expected result: The evaluation is successfully submitted, and a new evaluation record is added to the database; Actual result: The evaluation is successfully submitted, and a new evaluation record is added to the database;

[0140] View reviews use case: Expected result: Display all reviews of the product; Actual result: Display all reviews of the product;

[0141] Performance Test:

[0142] Concurrent submission of reviews: Expected result: All reviews were successfully submitted within the specified time; Actual result: 99 reviews were successfully submitted, and 1 review failed (due to network problems);

[0143] Query evaluation: Expected result: All query requests were successfully returned within the specified time; Actual result: 2 query requests failed (due to database interface problems);

[0144] Safety Testing:

[0145] SQL injection: Expected result: The system should prevent SQL injection attacks; Actual result: The system successfully prevents SQL injection attacks;

[0146] Cross-site scripting attack (XSS): Expected result: The system should prevent XSS attack; Actual result: The system successfully prevents XSS attack;

[0147] Compatibility Test:

[0148] Browser compatibility: Expected result: All features work properly on all browsers; Actual result: All features work properly on all browsers;

[0149] Device compatibility: Expected result: All functions work properly on different devices; Actual result: All functions work properly on different devices;

[0150] Test summary: pass rate 90%, failed test cases: 3, 1 concurrent submission of evaluation in performance test due to network problems; 2 viewing of evaluation in performance test due to database interface problems;

[0151] The integration and feedback module further includes a continuous integration process unit, a feedback mechanism unit and a test result visualization unit; the continuous integration process unit refers to integrating the test system into the interface process to achieve automated construction, testing and deployment; the feedback mechanism unit refers to providing feedback to the development team based on the test results and intelligent analysis results; the test result visualization unit refers to displaying the test results and system status through dashboards and visualization tools.

[0152] After the analysis results are fed back, the integration and feedback module performs process optimization. According to the feedback results and problems found during the testing process, the test process is optimized, including test strategy, use case design and priority allocation.

[0153] like Figure 2 As shown, the present invention provides a technical solution, a method for testing ICT application software based on artificial intelligence, and the method steps include the following contents:

[0154] S1. Parse the software requirement document, extract the test requirements, and generate the test plan and version control based on these requirements;

[0155] S2, build software usage scenarios based on the previously generated test plan, and automatically generate test cases covering these scenarios;

[0156] S3, integrate the generated test cases with the test tool, automatically execute the test cases, and record the test results;

[0157] S4. Analyze the collected test results, including but not limited to defect identification, resource consumption assessment, complexity analysis, result assessment and data analysis, and generate a test report based on the analysis results;

[0158] S5. Integrate the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback of test results and continuous testing process, and display test results and system status.

[0159] The software requirement analysis, extraction of test requirements, and generation of a test plan based on these test requirements include the following steps: parsing the software requirements described in natural language; extracting key information and test points from the requirement document; automatically generating a test plan based on the requirement model constructed by the modeling unit, the plan including a test plan, test objectives and test strategy, and implementing version control on the requirement document and the test plan to ensure that the test plan is always consistent with the latest requirements;

[0160] The method of constructing a usage scenario model according to a test plan and automatically generating test cases covering these scenarios includes the following steps: using a code analysis tool and an integrated development environment to construct a software usage scenario model according to the test requirements provided by the requirements analysis, and automatically generating test cases based on the scenario model constructed by scenario modeling and the test plan generated by the requirements analysis. The generated test cases will be used in the subsequent test execution process to ensure that the quality and performance of the software meet the predetermined requirements.

[0161] The test case is integrated with the test tool, and the test case is automatically executed and the test result is generated, which includes the following steps: the test tool integration unit is integrated with the mainstream test tool to prepare the environment for automated testing; according to the test environment and software usage scenario, the test tool integration unit automatically generates corresponding test scripts; the adaptive test integration unit is started to automatically execute the generated test cases in multiple test environments; during the test execution process, the adaptive test integration unit dynamically adjusts the priority of the test task according to various factors in the test project; by adjusting the priority, more efficient task deployment and resource allocation are achieved to optimize the test process and improve the test efficiency;

[0162] The dynamic priority adjustment process includes the following steps:

[0163] S1.1 Data collection and processing: Collect relevant data during the execution of the test task; analyze and process the collected data to extract key characteristic parameters.

[0164] S1.2 Construct a priority calculation model: Construct a priority calculation model. The task calculation formula of this model needs to consider five factors: complexity, importance, execution progress, resource consumption, and error rate of the test task. The following is the formula for calculating the dynamic priority of the test task:

[0165] P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T

[0166] (t) / T} δ +W5·{C(t) / C} ε

[0167] P(t) is the dynamic priority of the test task at time t;

[0168] I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient; the calculation formula of I(t) is as follows:

[0169] I(t)=I·[1+f(t)]·k

[0170] Where f(t) is the data corresponding to the factor at time t, and k is an adjustment factor;

[0171] R(t) is the resource consumption coefficient of the test task at time (t), and R is a baseline resource consumption coefficient; the calculation formula of R(t) is as follows:

[0172] R(t)=R·[1+g(t)]·k

[0173] Where g(t) is the factor at time t, and k is an adjustment factor;

[0174] E(t) is the error rate coefficient of the test task at time (t), and E is a baseline error rate coefficient;

[0175] The calculation formula is as follows:

[0176] E(t)=D(t) / D 总 ·E

[0177] Where D(t) is the number of defects found at time t, D 总 is the number of defects found during the entire testing process;

[0178] T(t) is the execution progress coefficient of the test task at time (t), and T is the maximum completion progress of the test task; the calculation formula is as follows:

[0179] T(t)=G(t) / G 总

[0180] Where G(t) is the number of test cases completed at time (t), G 总 is the total number of test cases for the test task;

[0181] C(t) is the risk factor of the test task at time (t), taking into account the potential impact of the task on system stability. C is a baseline risk factor. The calculation formula of C(t) is as follows:

[0182] C(t)=C·[1+k·[S(t)-S]]

[0183] Where S(t) represents the system stability data at time t, S represents the index value of the system in a stable state, and k is the adjustment factor;

[0184] The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1;

[0185] The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curve of each factor.

[0186] The test results are analyzed and a test report is generated based on the test results, including the following steps: preliminary processing of the test results to classify the test results into two categories: stable operation results and defect results; using an artificial intelligence algorithm to analyze the test results to identify potential software defects and abnormal data behavior; intelligently analyzing the identified test results, grading the severity of the defects, and making corresponding repair suggestions; generating a test report containing defect information and repair suggestions based on the results of preprocessing, identification and analysis, and ensuring that the test report can be output in multiple formats to meet different reporting requirements and reading habits.

[0187] The described steps of integrating the test system into the continuous integration / continuous deployment process of artificial intelligence software development, realizing intelligent feedback of test results and continuous progress of the test process, and displaying test results and system status include the following steps: integrating the test system into the interface process, configuring the process of automated building, testing and deployment, and ensuring that the test can be automatically executed after each code submission; after the execution is completed, the continuous integration process unit collects the test results and passes the results to the feedback mechanism unit; based on the test results and the results of the intelligent analysis, the necessary information is sorted out, and feedback is provided to the development team so as to keep abreast of software quality and performance issues; using dashboards and visualization tools, the test results and system status are converted into graphical displays, and intuitive charts and reports are generated; by viewing the charts and reports provided by the test result visualization unit, the test status and system status of the project can be quickly understood.

[0188] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The artificial intelligence-based ICT application software testing system is characterized by: Including demand analysis module, test case generation module, adaptive test execution module, intelligent analysis module, continuous integration and feedback module: The requirements analysis module is used to parse the software requirements document, extract the test requirements, and generate a test plan; the test case generation module can build software usage scenarios according to the test plan and automatically generate test cases covering different scenarios; the adaptive test execution module is integrated with the test tool to automatically test the generated test cases and record the test results; The intelligent analysis module analyzes the collected test results, including defect identification, resource consumption, complexity, result evaluation and data analysis, and generates a test report; the continuous integration and feedback module integrates the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback and continuous testing.

2. The artificial intelligence-based trust innovation application software testing system according to claim 1 is characterized in that: The requirement analysis module further includes a natural language processing unit, a requirement extraction unit, a test plan generation unit and a version control unit; the natural language processing unit is used to parse the software requirements described in natural language; the requirement extraction unit is used to extract key information and test points from the requirement document; the test plan generation unit automatically generates a test plan according to the requirement model constructed by the modeling unit, including a test plan, a test goal and a test strategy; The version control unit performs version control on the requirement documents and test plans to ensure that the test plans are consistent with the latest requirements.

3. The artificial intelligence-based trust innovation application software testing system according to claim 1 is characterized in that: The test case generation module further includes: a scenario modeling unit and a case generation unit; the scenario modeling unit is used to construct a software usage scenario model; the case generation unit is a model constructed based on the scenario modeling unit, and is used to automatically generate test cases.

4. The artificial intelligence-based trust innovation application software testing system according to claim 1 is characterized in that: The adaptive test execution module further includes: a test tool integration unit and an adaptive test integration unit; the test tool integration unit is used to automatically generate or assist in generating test scripts of automated test tools in different test tasks; the adaptive test integration unit is used to automatically execute test cases in multiple environments and adjust the priority of tasks according to different situations during the test process; The dynamic priority adjustment process includes the following steps: Data collection and processing: Collect relevant data during the execution of test tasks; analyze and process the collected data to extract key characteristic parameters. Build a priority calculation model: Build a priority calculation model. The task calculation formula of this model needs to consider five factors: complexity, importance, execution progress, resource consumption, and error rate of the test task. The following is the formula for calculating the dynamic priority of the test task: P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T(t) / T} δ +W5·{C(t) / C} ε P(t) is the dynamic priority of the test task at time t; I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient; the calculation formula of I(t) is as follows: I(t)=I·[1+f(t)]·k Where f(t) is the data corresponding to the factor at time t, and k is an adjustment factor; R(t) is the resource consumption coefficient of the test task at time (t), and R is a baseline resource consumption coefficient; The calculation formula of R(t) is as follows: R(t)=R·[1+g(t)]·k Where g(t) is the factor at time t, and k is an adjustment factor; E(t) is the error rate coefficient of the test task at time (t), and E is a baseline error rate coefficient; The calculation formula is as follows: E(t)=D(t) / D 总 ·E Where D(t) is the number of defects found at time t, D 总 is the number of defects found during the entire testing process; T(t) is the execution progress coefficient of the test task at time (t), and T is the maximum completion progress of the test task; the calculation formula is as follows: T(t)=G(t) / G 总 Where G(t) is the number of test cases completed at time (t), G 总 is the total number of test cases for the test task; C(t) is the risk factor of the test task at time (t), taking into account the potential impact of the task on system stability. C is a baseline risk factor. The calculation formula of C(t) is as follows: C(t)=C·[1+k·[S(t)-S]] Where S(t) represents the system stability data at time t, S represents the index value of the system in a stable state, and k is the adjustment factor; The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1; The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curve of each factor.

5. The artificial intelligence-based trust innovation application software testing system according to claim 1 is characterized in that: The intelligent analysis module further includes a result preprocessing unit, a defect identification unit, a data analysis unit and a report generation unit; the result preprocessing unit is to preprocess the test results; The defect identification unit refers to using artificial intelligence algorithms to analyze test results and identify potential software defects and abnormal data behaviors; the data analysis unit refers to performing intelligent analysis on test results, providing defect severity classification and recommended repair measures; the report generation unit generates test reports in multiple formats based on the analysis results.

6. The artificial intelligence-based trust innovation application software testing system according to claim 1 is characterized in that: The integration and feedback module further includes a continuous integration process unit, a feedback mechanism unit and a test result visualization unit; the continuous integration process unit refers to integrating the test system into the interface process to achieve automated construction, testing and deployment; the feedback mechanism unit refers to providing feedback to the development team based on the test results and intelligent analysis results; the test result visualization unit refers to displaying the test results and system status through dashboards and visualization tools.

7. The test method of information innovation application software based on artificial intelligence is characterized by: The method steps include the following: S1. Parse the software requirement document, extract the test requirements, and generate the test plan and version control based on these requirements; S2, build software usage scenarios based on the previously generated test plan, and automatically generate test cases covering these scenarios; S3, integrate the generated test cases with the test tool, automatically execute the test cases, and record the test results; S4. Analyze the collected test results, including but not limited to defect identification, resource consumption assessment, complexity analysis, result assessment and data analysis, and generate a test report based on the analysis results; S5. Integrate the test system into the continuous integration / continuous deployment process of artificial intelligence software development to achieve intelligent feedback of test results and continuous testing process, and display test results and system status.

8. The method for testing ICT application software based on artificial intelligence according to claim 7 is characterized in that: The software requirement analysis, extraction of test requirements, and generation of a test plan based on these test requirements include the following steps: parsing the software requirements described in natural language; extracting key information and test points from the requirement document; automatically generating a test plan based on the requirement model constructed by the modeling unit, the plan including a test plan, test objectives and test strategy, and implementing version control on the requirement document and the test plan to ensure that the test plan is always consistent with the latest requirements; The method of constructing a usage scenario model according to a test plan and automatically generating test cases covering these scenarios includes the following steps: using a code analysis tool and an integrated development environment to construct a software usage scenario model according to the test requirements provided by the requirements analysis, and automatically generating test cases based on the scenario model constructed by scenario modeling and the test plan generated by the requirements analysis. The generated test cases will be used in the subsequent test execution process to ensure that the quality and performance of the software meet the predetermined requirements.

9. The artificial intelligence-based ICT application software testing method according to claim 7 is characterized in that: The test case is integrated with the test tool, and the test case is automatically executed and the test result is generated, which includes the following steps: the test tool integration unit is integrated with the mainstream test tool to prepare the environment for automated testing; according to the test environment and software usage scenario, the test tool integration unit automatically generates corresponding test scripts; the adaptive test integration unit is started to automatically execute the generated test cases in multiple test environments; during the test execution process, the adaptive test integration unit dynamically adjusts the priority of the test task according to various factors in the test project; by adjusting the priority, more efficient task deployment and resource allocation are achieved to optimize the test process and improve the test efficiency; The dynamic priority adjustment process includes the following steps: S1.1 Data collection and processing: Collect relevant data during the execution of the test task; analyze and process the collected data to extract key characteristic parameters. S1.2 Construct a priority calculation model: Construct a priority calculation model. The task calculation formula of this model needs to consider five factors: complexity, importance, execution progress, resource consumption, and error rate of the test task. The following is the formula for calculating the dynamic priority of the test task: P(t)=W1·{I(t) / I} α +W2·{R(t) / R} β +W3·{1-E(t) / E} γ +W4·{T(t) / T} δ +W5·{C(t) / C} ε P(t) is the dynamic priority of the test task at time t; I(t) is the importance coefficient of the test task at time t, and I is a baseline importance coefficient; the calculation formula of I(t) is as follows: I(t)=I·[1+f(t)]·k Where f(t) is the data corresponding to the factor at time t, and k is an adjustment factor; R(t) is the resource consumption coefficient of the test task at time (t), and R is a baseline resource consumption coefficient; The calculation formula of R(t) is as follows: R(t)=R·[1+g(t)]·k Where g(t) is the factor at time t, and k is an adjustment factor; E(t) is the error rate coefficient of the test task at time (t), and E is a baseline error rate coefficient; The calculation formula is as follows: E(t)=D(t) / D 总 ·E Where D(t) is the number of defects found at time t, D 总 is the number of defects found during the entire testing process; T(t) is the execution progress coefficient of the test task at time (t), and T is the maximum completion progress of the test task; The calculation formula is as follows: T(t)=G(t) / G 总 Where G(t) is the number of test cases completed at time (t), G 总 is the total number of test cases for the test task; C(t) is the risk factor of the test task at time (t), taking into account the potential impact of the task on system stability. C is a baseline risk factor. The calculation formula of C(t) is as follows: C(t)=C·[1+k·[S(t)-S]] Where S(t) represents the system stability data at time t, S represents the index value of the system in a stable state, and k is the adjustment factor; The weights W1, W2, W3, W4, and W5 are the relative importance of each factor, and their sum is 1; The exponents α, β, γ, ε, and δ are parameters used to adjust the steepness of the influence curve of each factor.

10. The method for testing ICT application software based on artificial intelligence according to claim 7 is characterized in that: The test results are analyzed and a test report is generated based on the test results, including the following steps: preliminary processing of the test results to classify the test results into two categories: stable operation results and defect results; using an artificial intelligence algorithm to analyze the test results to identify potential software defects and abnormal data behavior; intelligently analyzing the identified test results, grading the severity of the defects, and making corresponding repair suggestions; generating a test report containing defect information and repair suggestions based on the results of preprocessing, identification and analysis, and ensuring that the test report can be output in multiple formats to meet different reporting requirements and reading habits. The described process of integrating the test system into the continuous integration / continuous deployment process of artificial intelligence software development, realizing intelligent feedback of test results and continuous progress of the test process, and displaying the test results and system status includes the following steps: integrating the test system into the interface process, configuring the process of automated building, testing and deployment, and ensuring that the test can be automatically executed after each code submission; after the execution is completed, the continuous integration process unit collects the test results and passes the results to the feedback mechanism unit; based on the test results and the results of the intelligent analysis, the necessary information is sorted out, and feedback is provided to the development team so as to keep abreast of software quality and performance issues; using dashboards and visualization tools, the test results and system status are converted into graphical displays, and intuitive charts and reports are generated; by viewing the charts and reports provided by the test result visualization unit, the test status and system status of the project can be quickly understood.

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