AI Test Script Troubleshooting With Automated Failure Repair
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Solution Overview
Problem
The complexity of software development and maintenance is exacerbated by the need for manual troubleshooting and maintenance of test scripts, which is time-consuming and prone to human error, especially in parallel development environments.
Innovation Solution
Utilizing a trained language model to automatically identify and update test scripts, reducing false positives and enhancing efficiency by diagnosing and repairing script failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting and maintenance of test scripts is performed, then human control and understanding of issues is maintained, but time consumption and human error increase
Solution Approach 1:
The system enables test scripts to self-diagnose and self-repair by automatically analyzing execution failures, identifying root causes, and generating corrected versions without human intervention. The language model processes failure data and autonomously updates test scripts, making the maintenance process self-serving rather than manually performed.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting with an automated language model-based system. The LLM processes test failure data, analyzes code patterns, and generates repairs through computational processes rather than human cognitive and manual operations, substituting the mechanical human-in-the-loop approach with an automated intelligent system.
2Reliability
If comprehensive testing of complicated software solutions is performed, then quality evaluation coverage is improved, but resource consumption and time requirements increase
Solution Approach 1:
The system implements a feedback loop where test execution results are automatically analyzed by the language model to identify failures, generate corrections, and update test scripts. This continuous feedback mechanism enables comprehensive quality evaluation while automating the maintenance process, improving both coverage and efficiency simultaneously.
Solution Approach 2:
The language model performs preliminary analysis of test failures and generates repair actions before manual intervention is required. By proactively identifying and correcting issues in test scripts before they affect comprehensive testing, the system maintains high quality coverage while reducing the resources needed for manual troubleshooting during extensive testing campaigns.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, software, and systems for automated identification of test script issues based on trained machine learning models. Data descriptive of a set of tests executed to test functionality of a software product and that has failed can be received. The received data can be processed using a language model to identify an issue in at least one of the test scripts that led to a failure. In response to receiving data indicative of the identified issue in a first test script, the first test script can be automatically updated based on using the data indicative of the identified issues. An updated first test script can be executed to determine whether the updated first test script is fixed. The updated first test script can be submitted for use when testing functionality of the software product at the testing framework.