Evolutionary testing for crash reproduction
Mozhan Soltani (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Annibale Panichella (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Arie Van Deursen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.