Strong higher order mutation-based test data generation

Authors:
Mark Harman;Yue Jia;William B. Langdon
Affiliations:
University College London, London, United Kingdom;University College London, London, United Kingdom;University College London, London, United Kingdom
Venue:
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Year:
2011

Citing 35
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Abstract

This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Execution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an empirical study using 17 programs, including production industrial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8% and 38% of those mutants left unkilled by the best performing previous approach.