Article

How Long Will It Take to Fix This Bug?

Authors:

Thomas Zimmermann,

Andreas ZellerAuthors Info & Claims

MSR '07: Proceedings of the Fourth International Workshop on Mining Software Repositories

Page 1

https://doi.org/10.1109/MSR.2007.13

Published: 20 May 2007 Publication History

Abstract

Predicting the time and effort for a software problem has long been a difficult task. We present an approach that automatically predicts the fixing effort, i.e., the person-hours spent on fixing an issue. Our technique leverages existing issue tracking systems: given a new issue report, we use the Lucene framework to search for similar, earlier reports and use their average time as a prediction. Our approach thus allows for early effort estimation, helping in assigning issues and scheduling stable releases. We evaluated our approach using effort data from the JBoss project. Given a sufficient number of issues reports, our automatic predictions are close to the actual effort; for issues that are bugs, we are off by only one hour, beating naïve predictions by a factor of four.

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cover image ACM Conferences

MSR '07: Proceedings of the Fourth International Workshop on Mining Software Repositories

May 2007

186 pages

ISBN:076952950X

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SIGSOFT: ACM Special Interest Group on Software Engineering

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IEEE Computer Society

United States

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Published: 20 May 2007

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Cited By

Graf-Vlachy LWagner S(2024)Cleaning Up Confounding: Accounting for Endogeneity Using Instrumental Variables and Two-Stage ModelsACM Transactions on Software Engineering and Methodology10.1145/367473033:8(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3674730
Caddy JTreude CShang WLamothe MWan Z(2024)Prioritising GitHub Priority LabelsProceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3663533.3664041(52-55)Online publication date: 10-Jul-2024
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Mahmud JRoychoudhury APaiva AAbreu RStorey M(2024)Toward Rapid Bug Resolution for Android AppsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639812(237-241)Online publication date: 14-Apr-2024
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Zhang QFang CMa YSun WChen Z(2023)A Survey of Learning-based Automated Program RepairACM Transactions on Software Engineering and Methodology10.1145/363197433:2(1-69)Online publication date: 6-Nov-2023
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Assi MHassan SGeorgiou SZou Y(2023)Predicting the Change Impact of Resolving Defects by Leveraging the Topics of Issue Reports in Open Source Software SystemsACM Transactions on Software Engineering and Methodology10.1145/359380232:6(1-34)Online publication date: 30-Sep-2023
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