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Cited by 375

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

How Long Will It Take to Fix This Bug?

Weiss, Cathrin, Premraj, Rahul, Zimmermann, Thomas, Zeller, Andreas·2007·Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)
DOI 10.1109/msr.2007.13
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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 naive predictions by a factor of four.

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Metadata

Authors
Weiss, C., Premraj, R., Zimmermann, T., & Zeller, A.
Venue
Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)
Publisher
IEEE
Published
2007-05
Pages
1-1
DOI
10.1109/msr.2007.13
OpenAlex
W2139410856
Cited by
375
References
8
FWCI
13.33
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: How Long Will It Take to Fix This Bug? https://app.cochat.ai/papers/10.1109/msr.2007.13
The paper
Weiss, C., Premraj, R., Zimmermann, T., & Zeller, A. (2007). How Long Will It Take to Fix This Bug? Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007). https://doi.org/10.1109/msr.2007.13

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.