Mayo, M. & Spacey, S. (2013). Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics. In G. Ruhe, and Y. Zhang (Eds.), Proceedings of 5th International Symposium, SSBSE 2013, St. Petersburg, Russia, August 24-26, 2013, LNCS 8084 (pp. 158-171). Berlin Heidelberg: Springer.
Permanent Research Commons link: http://hdl.handle.net/10289/7763
A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments.
This is the author's accepted version of a paper published by Springer in the series Lecture Notes in Computer Science (LNCS). The original publication is available at www.springerlink.com.