Regression testing is a practice aimed at providing confidence that, within software maintenance, the changes in the code base have introduced no faults in previously validated functionalities. With the software industry shifting towards iterative and incremental development with shorter release cycles, the straightforward approach of re-executing the entire test suite on each new version of the software is often unfeasible due to time and resource constraints. In such scenarios, Test Case Prioritization (TCP) strategies aim at providing an effective ordering of the test suite, so that the tests that are more likely to expose faults are executed earlier and fault detection is maximised even when test execution needs to be abruptly terminated due to external constraints. In this work, we propose Genetic-Diff, a TCP strategy based on a genetic algorithm featuring a specifically-designed crossover operator and a novel objective function that combines code coverage metrics with an analysis of changes in the code base. We empirically evaluate the proposed algorithm on several releases of three heterogeneous real-world, open source Java projects, in which we artificially injected faults, and compare the results with other state-of-the-art TCP techniques using fault-detection rate metrics. Findings show that the proposed technique performs generally better than the baselines, especially when there is a limited amount of code changes, which is a common scenario in modern development practices.
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Change-Aware Regression Test Prioritization using Genetic Algorithms
Semantic Scholar · Computer Science · 2022
Abstract
Regression testing is a practice aimed at providing confidence that, within software maintenance, the changes in the code base have introduced no faults in previously validated functionalities. With the software industry shifting towards iterative and incremental development with shorter release cycles, the straightforward approach of re-executing the entire test suite on each new version of the software is often unfeasible due to time and resource constraints. In such scenarios, Test Case Prioritization (TCP) strategies aim at providing an effective ordering of the test suite, so that the tests that are more likely to expose faults are executed earlier and fault detection is maximised even when test execution needs to be abruptly terminated due to external constraints. In this work, we propose Genetic-Diff, a TCP strategy based on a genetic algorithm featuring a specifically-designed crossover operator and a novel objective function that combines code coverage metrics with an analysis of changes in the code base. We empirically evaluate the proposed algorithm on several releases of three heterogeneous real-world, open source Java projects, in which we artificially injected faults, and compare the results with other state-of-the-art TCP techniques using fault-detection rate metrics. Findings show that the proposed technique performs generally better than the baselines, especially when there is a limited amount of code changes, which is a common scenario in modern development practices.