Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Data flow coverage and the C language
TAV4 Proceedings of the symposium on Testing, analysis, and verification
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ICSE '94 Proceedings of the 16th international conference on Software engineering
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Genetic Algorithms in Search, Optimization and Machine Learning
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
An experimental mutation system for Java
ACM SIGSOFT Software Engineering Notes
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Information and Software Technology
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In this research paper, an approach to fully automating the generation of test data for object-oriented programs fulfilling dataflow-based testing criteria and the subsequent evaluation of its fault-detection capability are presented. The underlying aim of the generation is twofold: to achieve a given dataflow coverage measure and to minimize the effort to reach this goal in terms of the number of test cases required. In order to solve the inherent conflict of this task, hybrid self-adaptive and multiobjective evolutionary algorithms are adopted. Our approach comprises the following steps: a preliminary activity provides support for the automatic instrumentation of source code in order to record the relevant dataflow information. Based on the insight gained hereby, test data sets are continuously enhanced towards the goals mentioned above. Afterwards, the generated test set is evaluated by means of mutation testing. Progress achieved so far in our ongoing project will be described in this paper.