Abstract
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.
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Oster, N. (2005). Automated Generation and Evaluation of Dataflow-Based Test Data for Object-Oriented Software. In: Reussner, R., Mayer, J., Stafford, J.A., Overhage, S., Becker, S., Schroeder, P.J. (eds) Quality of Software Architectures and Software Quality. QoSA SOQUA 2005 2005. Lecture Notes in Computer Science, vol 3712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558569_16
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DOI: https://doi.org/10.1007/11558569_16
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