Formal analysis of the effectiveness and predictability of random testing
Proceedings of the 19th international symposium on Software testing and analysis
Reducing qualitative human oracle costs associated with automatically generated test data
Proceedings of the First International Workshop on Software Test Output Validation
Multi-objective coevolutionary automated software correction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Cellular automata based test data generation
ACM SIGSOFT Software Engineering Notes
An orchestrated survey of methodologies for automated software test case generation
Journal of Systems and Software
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Previous approaches to search based test data generation tend to focus on coverage, rather than oracle cost. While there may be an aspiration that systems should have models, checkable specifications and/or contract driven development, this sadly remains an aspiration; in many real cases, system behaviour must be checked by a human. This painstaking checking process forms a significant cost, the oracle cost, which previous work on automated test data generation tends to overlook. One simple way to reduce oracle cost consists of reducing the number of tests generated. In this paper we introduce three algorithms which do this without compromising coverage achieved. We present the results of an empirical study of the effectiveness of the three algorithms on five benchmark programs containing non trivial search spaces for branch coverage. The results indicate that it is, indeed, possible to make reductions in the number of test cases produced by search based testing, without loss of coverage.