Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration

• Published in: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) pp. 1 - 12
• Main Authors: Bertolino, Antonia, Guerriero, Antonio, Miranda, Breno, Pietrantuono, Roberto, Russo, Stefano
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.10.2020
• Subjects: continuous integration; machine learning; Machine learning algorithms; regression testing; Reinforcement learning; Software algorithms; Software engineering; test prioritization; test selection; Testing; Time factors; Tuning
• ISSN: 1558-1225
• DOI: 10.1145/3377811.3380369

In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches. Two broad strategies for ML-based prioritization are learning-to-rank and what we call ranking-to-learn (i.e., reinforcement learning). Various ML algorithms can be applied in each strategy. In this paper we introduce ten of such algorithms for adoption in CI practices, and perform a comprehensive study comparing them against each other using subjects from the Apache Commons project. We analyze the influence of several features of the code under test and of the test process. The results allow to draw criteria to support testers in selecting and tuning the technique that best fits their context.