Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics

  • Michael Mayo
  • Simon Spacey
Conference paper

DOI: 10.1007/978-3-642-39742-4_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8084)
Cite this paper as:
Mayo M., Spacey S. (2013) Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics. In: Ruhe G., Zhang Y. (eds) Search Based Software Engineering. SSBSE 2013. Lecture Notes in Computer Science, vol 8084. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

regression testing test failure prediction program analysis machine learning genetic metric selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Mayo
    • 1
  • Simon Spacey
    • 1
  1. 1.Waikato UniversityHamiltonNew Zealand

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