ARTIFICIAL INTELLIGENCE (AI) FOR AN EFFICIENT TESTING APPLICATION. A Supported Dynamic Test Case Selection System and Method

Abstract

The system architecture processes historical test data, drawing from patterns and results. learning artificial intelligence model (12), insights from artificial intelligence model to interpret and transform these into actionable test plans an agent interface (14) that uses pre-configured commands, data analysis and Historical test results and metadata are necessary for test case prioritization. a central data repository (16) that stores data and dependencies An AI-powered dynamic test case for efficient test implementation. It relates to the electoral system and method.

Description

28390.29 1 TARIFF ARTIFICIAL INTELLIGENCE (AI) FOR AN EFFICIENT TESTING APPLICATION. A SUPPORTED DYNAMIC TEST CASE SELECTION SYSTEM AND METHOD 5 TECHNICAL FIELD The invention is an efficient testing application that incorporates an artificial intelligence model into the system architecture. an artificial intelligence (AI)-powered dynamic test case selection system and method for 10 It is related. PREVIOUS TECHNIQUE In the literature, traditional software testing involves test cases based on the changing nature of the code or 15 predefined charts without considering the results of past tests or is carried out according to fixed sets. This approach generally leads to inefficiencies and This leads to unnecessary repetition because test cases have a low probability of finding errors. It is run repeatedly. The increasing complexity of software systems and Continuous integration / continuous delivery (CI / CD) applications are increasingly common. With its adoption, it is easier to dynamically select the most relevant test cases. A smart solution is needed. In conclusion, all the problems mentioned above require an innovation in the relevant technical field. This has made it necessary to do so. 25 THE PURPOSE OF THE INVENTION The present invention aims to eliminate the aforementioned disadvantages and related artificial 30 for an efficient testing application to bring new advantages to the technical field It relates to an AI-assisted dynamic test case selection system and method. 28390.29 2 The purpose of the invention is to analyze historical test data and code changes. to leverage artificial intelligence models and to conduct only the most appropriate tests. The goal is to establish a system and method that provides this. Another purpose of the invention is to analyze error history, priority workflow, code changes, and prioritizing test situations based on factors such as test behavior patterns The goal is to achieve this with an artificial intelligence model. BRIEF DESCRIPTION OF THE INVENTION 10 All the purposes mentioned above and those that will emerge from the detailed explanation below. The current invention aims to implement artificial intelligence for an efficient testing application. It is an AI-supported dynamic test case selection system. Accordingly, its novelty lies in the system. Its architecture processes historical test data, learning from patterns and results. 15 interpreting the artificial intelligence model that created it, and the insights derived from the artificial intelligence model. and in advance to transform these into actionable test plans. an agent interface that uses configured commands, data analysis and test cases past test results, metadata, and other data necessary for prioritization It includes a central data repository that stores dependencies. Thus, the past 20 from an AI model to analyze test data and code changes to utilize and only conduct the most appropriate / important tests This is possible. Additionally, error history, priority workflow, code changes, and Prioritizing test situations based on factors such as test behavior patterns This can be achieved through an artificial intelligence model. Here, the artificial intelligence model is 25 It is a machine learning model (AI model, e.g., Llama3.1). The tool The interface is a Llama agent, the central data repository is an automation control panel data The database (Automation Dashboard Database) is also an automation data platform. It is the control panel (Automation Dashboard). 28390.29 3 In one configuration of the invention, developers and testers are provided with real-time providing feedback, the results and the status of the tests conducted. It includes an interactive data platform that displays the results. Thus, the test created... The plan is transferred from the agent interface to the data platform for execution. This In this case, visual feedback and measurements are provided to test experts from the data platform. 5 by ensuring that errors are identified and resolved quickly. It can be provided. In one application of the invention, historical test data is retrieved from a central data pool. the collection and transmission of data to the artificial intelligence model; 10 in the artificial intelligence model test plan based on pre-configured commands using generated insights Passing the generated agent through the interface; approximate execution results The results are returned to the data platform for visualization and further action. The process includes the steps for sending the message. Thus, in accordance with the method... An AI-powered dynamic test case for efficient test implementation 15 The choice is provided. In one application of the invention, data is transmitted to an artificial intelligence model. Generating insights involves data collection in the artificial intelligence algorithm within the model. Test case prioritization and selection of selection criteria, dynamic 20 It is characterized by being done by providing updates. Thus, the most relevant Artificial intelligence provides a smarter solution for dynamically selecting test cases. This can be achieved through the model. In one application of the invention, the selection criteria are failure frequency, criticality, and 25 Priority, test case history and transition from passed to failed status, dependencies between test cases, code change relevance, and at least one criterion selected from a group of unpredictable behavioral criteria This improves the functional state of the artificial intelligence model. BRIEF DESCRIPTION OF THE FIGURE 28390.29 4 Figure 1 shows an artificial intelligence (AI)-powered dynamic for an efficient testing application. A schematic representation of the test case selection system is provided. Figure 2 shows an AI-powered dynamic 5 for an efficient testing application. A flowchart representation of the test case selection method is provided. REFERENCE NUMBERS GIVEN IN THE FIGURE Automatic test case selection system 12 Artificial intelligence models 14 Tool Interface 16 Central data pools 18 Data platforms Dynamic test case selection method 22. Obtaining historical test data from a central data repository. 24. Transmitting data to the artificial intelligence model. 26 Insights generated from artificial intelligence models 28 Passing through the vehicle interface Sending the results to the data platform. DETAILED DESCRIPTION OF THE INVENTION This detailed explanation describes the invention as an artificial intelligence for an efficient testing application. An AI-supported dynamic test case selection system and method is only relevant to the subject. with examples that will not create any limiting effect on better understanding. It is explained. Figure 1 shows an artificial intelligence (AI)-powered dynamic for an efficient testing application. A schematic representation of the test case selection system is given. An efficient test. An artificial intelligence (AI)-powered dynamic test case selection system for the application 20 28390.29 (10), processing past test data in system architecture, from patterns and results It has an artificial intelligence model (12) that learns. Artificial intelligence interpreting the insights from the model and testing them in an actionable way a tool that uses pre-configured commands to convert plans It includes the interface (14). In addition, data analysis and test case prioritization 5 storing past test results, metadata, and dependencies necessary for There is a central data repository (16). In the invention, an interactive data platform (18) real-time feedback to developers and testers It provides visualization of the results and the status of the tests conducted. This makes it possible. Here the data platform (18) is an automation control panel 10 The invention concerns artificial intelligence (AI) for an efficient testing application. AI-powered dynamic test case selection system By presenting a test case selection approach, the testing process is analyzed using historical data and code. optimize by leveraging changes and test case performance. This involves using artificial intelligence to conduct only the most relevant tests. By doing so, it increases efficiency, improves error detection, and eliminates unnecessary testing. It reduces repetitions. Figure 2 shows an artificial intelligence (AI)-powered dynamic range for an efficient testing application. The flowchart representation of the test case selection method is given in Figure 2. an artificial intelligence (AI)-powered dynamic for an efficient testing application demonstrated The sequence of steps for selecting the test case method is given below: - First, past test data should be retrieved from the central data pool (22) is provided. - Then the data is transmitted to the artificial intelligence model (24). 25 - Later, the insights generated in the artificial intelligence model (26) were previously from the tool interface that creates a test plan based on configured commands (28) is ensured. - Approximately visualize execution results and provide data for further action. The results are sent back to the platform (30). 30 28390.29 6 Insights were obtained by transmitting the data to the artificial intelligence model (24) in the invention. data collection and testing in the artificial intelligence algorithm of the model Prioritization and selection of selection criteria, dynamic updates. This is ensured by providing. Furthermore, the selection criterion in the invention is failure. Frequency, criticality and priority, test case history and pass / fail status 5 Transition to the 'yes' state, dependencies between test cases, code change relevance at least one selected from a group consisting of level and unpredictable behavior criteria It is a criterion. The invention concerns an artificial intelligence (AI)-powered 10 for an efficient testing application. Dynamic test case selection system and method with artificial intelligence (AI) algorithms Daily or weekly based on past test results and other relevant data. A system is provided that dynamically selects test cases for execution. The system includes information such as failure history, priority, code changes, and test behavior patterns. Artificial intelligence (AI) 15 to prioritize test cases according to factors It uses the following criteria to determine which test the artificial intelligence (AI) system will perform. It is used to specify which conditions will be executed: - Test Cases That Frequently Fail: AI has historically frequently failed. Identifying failed test cases and revealing errors. Since the probabilities are higher, 20 is given priority for execution. It places. - High Priority Test Cases: Test cases with a higher priority. AI to ensure critical functions are tested (as assigned in the test plan) It is always chosen by them. - Test Cases That Went from Success to Failure: 25 that consistently passed However, test cases that later turn out to be failures reveal new errors or to be retested by AI as it may show regressions It is marked. - Clustering Test Cases with Dependency Errors: AI, dependencies clusters of test cases that tend to fail due to various reasons 28390.29 7 to identify the most relevant test in the cluster and optimize test execution It chooses its status. - Code Change-Based Selection: The AI ​​system continuously selects code changes. we are monitoring and testing the situation and the affected sections of the warehouse. This updates the mapping so that the most relevant test 5 is available for the latest changes. This allows for the selection of their options. - Test Cases That Go From Failure to Success: Failure for a Long Period recent test results, changes in status AI can highlight stability improvements or new problems. It is marked by 10. - Unpredictable Test Cases: AI, between success and failure. to identify test situations that change unpredictably and intermittently To catch problems, prioritize them for systematic execution. It places. - Mapping Test Cases to Features: AI maps test cases to the software's critical 15 mapping to their properties, selecting those that are most dependent on core functions, and It ensures the coverage of high-priority areas. - Selection of Newly Added Test Cases: Newly added test cases, test AI to ensure they are tested immediately after being included in the plan They are automatically included in the selection process directed by [the relevant authority]. 20 - Test Cases with a Long History of Success: Certain test cases are consistent. Even if it somehow gets through, AI has previously been associated with error detection. This ensures that they are selected periodically. - Error Discovery Association: Tests previously associated with error discovery. Even after consistently passing the tests, the test team's comprehensive 25 To ensure its continued presence, it remains in the AI's selection pool.

Claims

28390.29 8 REQUESTS 1. An AI-powered dynamic test for an efficient testing application. The status selection system (10) has the feature of past testing in the system architecture. AI that processes data and learns from patterns and results 5 interpreting the insights from the artificial intelligence model (12) and using them in advance to transform into actionable test plans a tool interface (14) that uses configured commands, data analysis and testing past test results and metadata necessary for prioritizing the situation and includes a central data repository (16) that stores dependencies. 10 2. An artificial intelligence (AI)-powered approach for an efficient testing implementation that complies with Claim 1. Dynamic test case selection system (10) is a feature that allows developers and testers to choose from it. providing real-time feedback to experts on the results and tests conducted. an interactive data platform (18) 15 that displays the status of their status It includes.

3. An AI-powered dynamic test for an efficient testing implementation. The situation is the selection method (20) and its feature is; central data of past test data. taking from the pool (22) and transmitting the data to the artificial intelligence model (24); 20 the insights generated in the artificial intelligence model (26) pre-structured passing through the tool interface that creates a command-based test plan (28); approximate execution results visualization and data for further action. The process of sending the results back to the platform includes (30) steps.

4. An artificial intelligence (AI)-assisted system for an efficient testing implementation that complies with Claim 3. Dynamic test case selection method (20) and its feature is; data artificial intelligence by transmitting it to the model (24) and generating insights in the model artificial Data collection, test case prioritization, and selection in intelligence algorithms. The selection of the criterion is done by providing dynamic updates, 30 It is the characterization of the situation. 28390.29 9 5. An artificial intelligence (AI)-powered approach for an efficient testing implementation that complies with Claim 4. The dynamic test case selection method (20) has the following feature: selection criteria failure frequency, criticality and priority, test case history, and pass rate. The transition from failed to failed status, 5 between test cases. dependencies, code change relevance, and unpredictable behavior There must be at least one criterion selected from the group of criteria.

Images