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 in uence 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 ts their context.

Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration / Bertolino, Antonia; Guerriero, Antonio; Miranda, Breno; Pietrantuono, Roberto; Russo, Stefano. - (2020), pp. 1261-1272. (Intervento presentato al convegno 42nd International Conference on Software Engineering tenutosi a Seoul, Korea nel 27 June 2020 through 19 July 2020) [10.1145/3377811.3380369].

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

Antonio Guerriero;Roberto Pietrantuono;Stefano Russo
2020

Abstract

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 in uence 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 ts their context.
2020
978-145037121-6
Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration / Bertolino, Antonia; Guerriero, Antonio; Miranda, Breno; Pietrantuono, Roberto; Russo, Stefano. - (2020), pp. 1261-1272. (Intervento presentato al convegno 42nd International Conference on Software Engineering tenutosi a Seoul, Korea nel 27 June 2020 through 19 July 2020) [10.1145/3377811.3380369].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/813513
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