Abstract
Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.
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Notes
- 1.
Note that our primary fitness function is essentially the mean wef computed for faults in the training data-set.
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Acknowledgements
This research was supported by the Korean MSIT(Ministry of Science and ICT), under the National Program for Excellence in SW (2016-0-00018), supervised by the IITP(Institute for Information & communications Technology Promotion).
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Choi, K., Sohn, J., Yoo, S. (2018). Learning Fault Localisation for both Humans and Machines Using Multi-objective GP. In: Colanzi, T., McMinn, P. (eds) Search-Based Software Engineering. SSBSE 2018. Lecture Notes in Computer Science(), vol 11036. Springer, Cham. https://doi.org/10.1007/978-3-319-99241-9_20
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