Nighthawk: a two-level genetic-random unit test data generator

Authors:
James H. Andrews;Felix C. H. Li;Tim Menzies
Affiliations:
University of Western Ontario, London, ON, Canada;University of Western Ontario, London, ON, Canada;West Virginia University, Morgantown, WV
Venue:
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Year:
2007

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Abstract

Randomized testing has been shown to be an effective method fortesting software units. However, the thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. In this paper, we describe a system which uses agenetic algorithm to find parameters for randomized unit testing that optimize test coverage. We compare our coverage results to previous work, and report on case studies and experiments on system options