Defect Prediction Guided Search-Based Software Testing

Anjana Perera, Aldeida Aleti, Marcel Böhme, Burak Turhan

Abstract

Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defect prediction technique.

BibTeX

@inproceedings{Perera-al:ASE20,
  author    = {Anjana Perera and
               Aldeida Aleti and
               Marcel B{\"{o}}hme and
               Burak Turhan},
  title     = {Defect Prediction Guided {Search-Based} Software Testing},
  booktitle = {ASE},
  pages     = {448--460},
  publisher = {{IEEE}},
  year      = {2020},
}

Defect Prediction Guided Search-Based Software Testing

Abstract

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