Test Impact Prediction for Code Commits

Pradeepkumar Palanisamy ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 5, 28 - 41

Abstract: In fast-paced software development environments, the efficiency of Continuous Integration (CI) pipelines is frequently hampered by the escalating time required to execute comprehensive test suites. This paper presents an AI-driven framework for Test Impact Prediction (TIP) designed to intelligently identify and prioritize only the most relevant test cases affected by a specific code commit, thereby significantly reducing test execution time without compromising quality. By training sophisticated AI models on a rich array of data—including detailed code coverage metrics, granular commit differences (diffs), historical mappings between code changes and affected tests, and intricate dependency graphs of both code and tests—our system accurately predicts which tests are most likely to be impacted. This intelligent prediction enables the dynamic selection and prioritization of tests within CI pipelines, leading to an anticipated reduction in test execution time by over 50% with minimal risk of regressions. This approach not only accelerates feedback cycles for developers but also optimizes computational resource utilization, fostering a more agile and efficient development workflow crucial for modern software delivery.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Continuous Integration; Test Impact Analysis (search for similar items in EconPapers)
Date: 2025
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