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Automated program repair (APR) tools have unlocked the potential for therapid rectification of codebase issues. However, to encourage wider adoption ofprogram repair in practice, it is necessary to address the usability concernsrelated to generating irrelevant or out-of-context patches. When softwareengineers are presented with patches they deem uninteresting or unhelpful, theyare burdened with more "noise" in their workflows and become less likely toengage with APR tools in future. This paper presents a novel approach tooptimally time, target, and present auto-generated patches to softwareengineers. To achieve this, we designed, developed, and deployed a new tooldubbed B-Assist, which leverages GitHub's Suggested Changes interface toseamlessly integrate automated suggestions into active pull requests (PRs), asopposed to creating new, potentially distracting PRs. This strategy ensuresthat suggestions are not only timely, but also contextually relevant anddelivered to engineers most familiar with the affected code. Evaluation amongBloomberg software engineers demonstrated their preference for this approach.From our user study, B-Assist's efficacy is evident, with the acceptance rateof patch suggestions being as high as 74.56%; engineers also found thesuggestions valuable, giving usefulness ratings of at least 4 out of 5 in 78.2%of cases. Further, this paper sheds light on persisting usability challenges inAPR and lays the groundwork for enhancing the user experience in future APRtools.

User-Centric Deployment of Automated Program Repair at Bloomberg