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

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 alternat...

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Vydáno v:2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) s. 1 - 12
Hlavní autoři: Bertolino, Antonia, Guerriero, Antonio, Miranda, Breno, Pietrantuono, Roberto, Russo, Stefano
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.10.2020
Témata:
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
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Shrnutí: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.
ISSN:1558-1225
DOI:10.1145/3377811.3380369