Abstract
Software development relies on end-to-end (E2E) testing to ensure quality and reliability. However, E2E tests are often time-consuming and expensive. This disclosure describes automated techniques to comprehensively perform end-to-end (E2E) testing of software in an adaptive manner. Based on analyzing code changes, the techniques intelligently recommend a subset of tests to achieve targeted E2E testing. The tests enable developers to develop with more confidence. Employing machine learning (ML) models trained over a combined dataset of static code analysis, code coverage metrics from multiple system-under-test (SUT) configurations, historical bug data, and an integrated user feedback mechanism, the most relevant tests for a given code change are identified. This targeted approach can significantly reduce software development time and effort while maximizing bug-free production code, leading to faster development cycles and higher software quality. The adaptive nature of the approach, incorporating real-time user feedback, ensures continuous improvement in recommendation accuracy and adaptability to evolving codebases.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
n/a, "Intelligent, End-to-End Test Selection Using Artificial Intelligence", Technical Disclosure Commons, (May 13, 2025)
https://www.tdcommons.org/dpubs_series/8121