- author = "David Williams and James Callan and Serkan Kirbas and Sergey Mechtaev and Justyna Petke and Thomas Prideaux-Ghee and Federica Sarro",
- title = "User-Centric Deployment of Automated Program Repair at {Bloomberg}",
- booktitle = "46th International Conference on Software Engineering - Software Engineering in Practice track (ICSE-SEIP 2024)",
- year = "2024",
- address = "Lisbon",
- month = "14-20 " # apr,
- note = "Distinguished Paper Award Winner",
- keywords = "genetic algorithms, genetic programming, genetic improvement, APR",
-
URL = "
https://arxiv.org/abs/2311.10516",
-
URL = "https://discovery.ucl.ac.uk/id/eprint/10187233/",
- size = "11 pages",
- abstract = "Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice, it is necessary to address the usability concerns related to generating irrelevant or out-of-context patches. When software engineers are presented with patches they deem uninteresting or unhelpful, they are burdened with more noise in their workflows and become less likely to engage with APR tools in future. optimally time, target, and present auto-generated patches to software engineers. To achieve this, we designed, developed, and deployed a new tool dubbed B-Assist, GitHub Suggested Changes interface to seamlessly integrate automated suggestions into active pull requests (PRs), as opposed to creating new, potentially distracting PRs. relevant and delivered to engineers most familiar with the affected code. Evaluation among Bloomberg software engineers demonstrated their preference for this approach. From our user study, B-Assist efficacy is evident, with the acceptance rate of patch suggestions being as high as 74.56percent engineers also found the suggestions valuable, giving usefulness ratings of at least 4 out of 5 in 78.2percent of cases.",
-
notes = "Not GP
See also \cite{williams2023usercentric}",
