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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Winter, Emily Rowan
Nowack, Vesna
Bowes, David
Counsell, Steve
Hall, Tracy
Haraldsson, Saemundur
Woodward, John
Kirbas, Serkan
Windels, Etienne
McBello, Olayori
Atakishiyev, Abdurahman
Kells, Kevin
Pagano, Matthew
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Title: Towards Developer-Centered Automatic Program Repair: Findings from Bloomberg
Citation: Winter ER, Nowack V, Bowes D, Counsell S, Hall T, Haraldsson S, Woodward J, Kirbas S, Windels E, McBello O, Atakishiyev A, Kells K & Pagano M (2022) Towards Developer-Centered Automatic Program Repair: Findings from Bloomberg. In: <i>ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering</i>. ESEC/FSE ’22, Singapore, 14.11.2022-18.11.2022. New York: ACM, pp. 1578-1588.
Issue Date: Nov-2022
Date Deposited: 7-Nov-2022
Conference Name: ESEC/FSE ’22
Conference Dates: 2022-11-14 - 2022-11-18
Conference Location: Singapore
Abstract: This paper reports on qualitative research into automatic program repair (APR) at Bloomberg. Six focus groups were conducted with a total of seventeen participants (including both developers of the APR tool and developers using the tool) to consider: the development at Bloomberg of a prototype APR tool (Fixie); developers' early experiences using the tool; and developers' perspectives on how they would like to interact with the tool in future. APR is developing rapidly and it is important to understand in greater detail developers' experiences using this emerging technology. In this paper, we provide in-depth, qualitative data from an industrial setting. We found that the development of APR at Bloomberg had become increasingly user-centered, emphasising how fixes were presented to developers, as well as particular features, such as cus-tomisability. From the focus groups with developers who had used Fixie, we found particular concern with the pragmatic aspects of APR, such as how and when fixes were presented to them. Based on our findings, we make a series of recommendations to inform future APR development, highlighting how APR tools should 'start small', be customisable, and fit with developers' workflows. We also suggest that APR tools should capitalise on the promise of repair bots and draw on advances in explainable AI.
Status: VoR - Version of Record
Rights: This work is licensed under a Creative Commons Attribution 4.0 International License (
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