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Speeding Up Mutation Testing via the Cloud: Lessons Learned for Further Optimisations
University Antwerpen, Belgium.
University Antwerpen, Belgium.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7879-4371
Ericsson AB, Sweden.
2018 (English)In: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2018, article id 26Conference paper, Published paper (Refereed)
Abstract [en]

Background: Mutation testing is the state-of-the-art technique for assessing the fault detection capacity of a test suite. Unfortunately, it is seldom applied in practice because it is computationally expensive. We witnessed 48 hours of mutation testing time on a test suite comprising 272 unit tests and 5,258 lines of test code for testing a project with 48,873 lines of production code. Aims: Therefore, researchers are currently investigating cloud solutions, hoping to achieve sufficient speed-up to allow for a complete mutation test run during the nightly build. Method: In this paper we evaluate mutation testing in the cloud against two industrial projects. Results: With our proof-of-concept, we achieved a speed-up between 12x and 12.7x on a cloud infrastructure with 16 nodes. This allowed to reduce the aforementioned 48 hours of mutation testing time to 3.7 hours. Conclusions: We make a detailed analysis of the delays induced by the distributed architecture, point out avenues for further optimisation and elaborate on the lessons learned for the mutation testing community. Most importantly, we learned that for optimal deployment in a cloud infrastructure, tasks should remain completely independent. Mutant optimisation techniques that violate this principle will benefit less from deploying in the cloud.

Place, publisher, year, edition, pages
2018. article id 26
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-36461DOI: 10.1145/3239235.3240506Scopus ID: 2-s2.0-85061485994OAI: oai:DiVA.org:ri-36461DiVA, id: diva2:1266282
Conference
12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-29Bibliographically approved

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Publisher's full textScopushttp://doi.acm.org/10.1145/3239235.3240506

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Borg, Markus

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