Predicting Mutation Score Using Source Code and Test Suite Metrics*

by Kevin Jalbert, Jeremy S. Bradbury

* RAISE 2012 Best Paper Award.

Abstract

RAISE 2012 presentationMutation testing has traditionally been used to evaluate the effectiveness of test suites and provide confidence in the testing process. Mutation testing involves the creation of many versions of a program each with a single syntactic fault. A test suite is evaluated against these program versions (mutants) in order to determine the percentage of mutants a test suite is able to identify (mutation score). A major drawback of mutation testing is that even a small program may yield thousands of mutants and can potentially make the process cost prohibitive. To improve the performance and reduce the cost of mutation testing, we propose a machine learning approach to predict mutation score based on a combination of source code and test suite metrics.

Bibliographic Information [Bibtex format]

@inproceedings{JB12,
Author = {Kevin Jalbert and Jeremy S. Bradbury},
Title = {Predicting Mutation Score Using Source Code and Test Suite Metrics},
Booktitle = {Proc. of the Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2012)},
Month = {Jun.},
Year = {2012}
}

Paper: [PDF]   Presentation: [PDF]

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