Intelligent self-adaptive software testing system and method

Abstract

The invention discloses an intelligent adaptive software test system and method. The system comprises a demand analysis module, a test case generation module, a test execution module and a result analysis module. According to the method, an intelligent self-adaptive software test system is applied, and the method comprises the following steps that a demand analysis module automatically analyzes a software demand document and extracts a test demand; a test case generation module automatically generates a test case according to the extracted test requirements; the test execution module executes the test case and stores a test result in a database; and the result analysis module analyzes the test result, locates software defects and provides improvement suggestions. According to the method, the automation degree of software testing is improved, manual intervention is reduced, and the generation speed and coverage rate of test cases are improved; through a machine learning technology, the accuracy and efficiency of test result analysis are improved; the method adapts to frequent change of software requirements and quickly responds to change of test requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software testing, and in particular to an intelligent self-adaptive software testing system and method. Background Art

[0002] Existing software testing technology mainly relies on manual writing of test cases and execution of tests. This method has the following problems: 1. Test case writing is time-consuming and error-prone, especially when software requirements change frequently; 2. Test coverage is difficult to ensure, and key test scenarios may be missed; 3. Inefficient testing, especially in large-scale software projects; 4. The analysis of test results depends on the experience of the tester, which may lead to inaccurate problem location.

[0003] There is currently no effective solution to the above problems. Summary of the invention

[0004] In response to the above technical problems in the related technology, the present invention proposes an intelligent adaptive software testing system and method, which solves the problems of low efficiency, insufficient coverage, inaccurate result analysis, etc. existing in the existing software testing technology, and can overcome the above-mentioned shortcomings of the prior art.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows: An intelligent adaptive software testing system, comprising a demand analysis module, a test case generation module, a test execution module and a result analysis module; The requirement analysis module is used to automatically parse the software requirement document through natural language processing technology to extract key information; The test case generation module is used to automatically generate test cases based on the artificial intelligence algorithm and the key information provided by the demand analysis module; The test execution module is used to automatically execute the test cases generated by the test case generation module, collect the test results, and store them in the database; The result analysis module is used to analyze the data collected by the test execution module using machine learning technology, identify software defects, and provide optimization suggestions.

[0006] Furthermore, artificial intelligence algorithms include deep learning.

[0007] An intelligent self-adaptive software testing method, using an intelligent self-adaptive software testing system, comprises the following steps: S1 requirement analysis module automatically parses software requirement documents and extracts test requirements; The S2 test case generation module automatically generates test cases based on the extracted test requirements; The S3 test execution module executes the test cases and stores the test results in the database; The S4 result analysis module analyzes the test results, locates software defects, and makes improvement suggestions.

[0008] The beneficial effects of the present invention are as follows: the present invention improves the automation level of software testing, reduces manual intervention, and improves the generation speed and coverage of test cases; improves the accuracy and efficiency of test result analysis through machine learning technology; adapts to frequent changes in software requirements and quickly responds to changes in test requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 It is a structural block diagram of the intelligent adaptive software testing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0012] like Figure 1 As shown, an intelligent adaptive software testing system according to an embodiment of the present invention includes a demand analysis module, a test case generation module, a test execution module and a result analysis module; The requirement analysis module is used to automatically parse the software requirement document through natural language processing technology to extract key information; The test case generation module is used to automatically generate test cases based on the artificial intelligence algorithm and the key information provided by the demand analysis module; The test execution module is used to automatically execute the test cases generated by the test case generation module, collect the test results, and store them in the database; The result analysis module is used to analyze the data collected by the test execution module using machine learning technology, identify software defects, and provide optimization suggestions.

[0013] Artificial intelligence algorithms include deep learning.

[0014] An intelligent self-adaptive software testing method, using an intelligent self-adaptive software testing system, comprises the following steps: S1 requirement analysis module automatically parses software requirement documents and extracts test requirements; The S2 test case generation module automatically generates test cases based on the extracted test requirements; The S3 test execution module executes the test cases and stores the test results in the database; The S4 result analysis module analyzes the test results, locates software defects, and makes improvement suggestions.

[0015] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail below through specific usage methods.

[0016] When used specifically, the specific embodiments according to the present invention are as follows: like Figure 1 As shown, the intelligent adaptive software testing system includes the following modules: Requirements analysis module: This module automatically parses software requirements documents and extracts key information through natural language processing technology.

[0017] Test case generation module: Based on artificial intelligence algorithms, such as deep learning, test cases are automatically generated according to the key information provided by the requirements analysis module.

[0018] Test execution module: This module automatically executes the test cases generated by the test case generation module, collects the test results, and stores them in the database.

[0019] Result analysis module: Uses machine learning technology to analyze the data collected by the test execution module, identify software defects, and provide optimization suggestions.

[0020] The specific steps are as follows: (1) The requirements analysis module automatically parses the software requirements document and extracts the test requirements.

[0021] (2) The test case generation module automatically generates test cases based on the extracted test requirements.

[0022] (3) The test execution module executes the test cases and stores the test results in the database.

[0023] (4) The result analysis module analyzes the test results, locates software defects, and makes improvement suggestions.

[0024] Example 1: Application in software testing of mobile banking applications Step 1: Requirements Analysis The requirements analysis module collects the requirements documents, design documents and source code of the mobile banking application, including detailed descriptions of functions such as user registration, login, transfer, and inquiry.

[0025] Step 2: Test case generation The test case generation module uses natural language processing technology to analyze the requirements document and automatically generate the following test cases: a. User registration test cases, including test scenarios with different input conditions (such as correct email address, incorrect email address, correct mobile phone number, incorrect mobile phone number, etc.); b. Login function test cases, covering password login, fingerprint recognition login and abnormal situation handling; c. Transfer function test cases, including normal transfer, transfer limit test, transfer error handling, etc.

[0026] Step 3: Test Execution The test execution module automatically executes the test cases generated above and collects the test results in the simulated mobile banking application environment.

[0027] Step 4: Result Analysis The result analysis module analyzes the collected test results and uses machine learning algorithms to identify potential defects, such as: a. When a user registers, if the verification code fails to be sent, the analysis module will identify it as a potential defect and give improvement suggestions; b. During the login function test, if the fingerprint recognition success rate is lower than expected, the analysis module will recommend optimizing the recognition algorithm.

[0028] Example 2: Application in stress testing of e-commerce websites Step 1: Requirements Analysis The requirements analysis module obtains the performance requirements of the e-commerce website, including indicators such as the maximum number of concurrent users, response time, and system throughput.

[0029] Step 2: Test case generation The test case generation module automatically designs the following stress test cases based on performance requirements: a. Website response time test under different concurrent user numbers; b. Test the order processing capability under high concurrency; c. System resource utilization monitoring test.

[0030] Step 3: Test Execution The test execution module executes test cases in a simulated high-concurrency environment, uses automated tools to simulate user behavior, and monitors website performance indicators.

[0031] Step 4: Result Analysis The result analysis module analyzes the test data and identifies performance bottlenecks, such as: If the website is found to have a long response time when the number of concurrent users reaches a certain threshold, the analysis module will make suggestions for optimizing database queries or increasing server resources; If system resource usage is too high, the analysis module will recommend code optimization or hardware upgrades.

[0032] Through the above embodiments, the present invention demonstrates its application in different types of software testing, proving that it can automatically generate and execute test cases according to different testing requirements, and provide effective optimization suggestions through intelligent analysis, thereby improving software quality.

[0033] To sum up, with the help of the above-mentioned technical scheme of the present invention, the present invention improves the degree of automation of software testing, reduces manual intervention, and improves the generation speed and coverage of test cases; improves the accuracy and efficiency of test result analysis through machine learning technology; adapts to frequent changes in software requirements and quickly responds to changes in test requirements.

[0034] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent adaptive software testing system, characterized in that: It includes requirement analysis module, test case generation module, test execution module and result analysis module; The requirement analysis module is used to automatically parse the software requirement document through natural language processing technology to extract key information; The test case generation module is used to automatically generate test cases based on the artificial intelligence algorithm and the key information provided by the demand analysis module; The test execution module is used to automatically execute the test cases generated by the test case generation module, collect the test results, and store them in the database; The result analysis module is used to analyze the data collected by the test execution module using machine learning technology, identify software defects, and provide optimization suggestions.

2. The intelligent adaptive software testing system according to claim 1, characterized in that: Artificial intelligence algorithms include deep learning.

3. An intelligent adaptive software testing method, characterized in that: The intelligent adaptive software testing system of claim 1 comprises the following steps: S1 requirement analysis module automatically parses software requirement documents and extracts test requirements; The S2 test case generation module automatically generates test cases based on the extracted test requirements; The S3 test execution module executes the test cases and stores the test results in the database; The S4 result analysis module analyzes the test results, locates software defects, and makes improvement suggestions.

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