Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Test amplification

With respect to self-healing, we proposed a new algorithm for test amplification. Test amplification consists of exploiting the knowledge of test methods, in which developers embed input data and expected properties, in order to enhance these tests [22].

We proposed a new approach based on test inputs transformation and assertions generation to amplify test suites, and implemented this approach in the DSpot software tool that we created [21]. By evaluating DSpot on open-source projects from GitHub, we showed that we improve the mutation score of test suites. These improvements have been proposed to developers through pull requests: their feedbacks show that they value the output of DSpot by accepting to integrate amplified test methods into their test suite. This proves that DSpot can improve the quality of the test suite of real projects. We also showed that DSpot can generate amplified test methods that specify behavioral changes, and can generate amplified test methods to improve the ability to detect potential regressions.

These results have been obtained in the context of the STAMP H2020 project and in the context of the PhD thesis of Benjamin Danglot [11] defended in November 2019.