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Bug-fix time prediction models: can we do better?

Authors:

Pamela Bhattacharya,

Iulian NeamtiuAuthors Info & Claims

MSR '11: Proceedings of the 8th Working Conference on Mining Software Repositories

Pages 207 - 210

https://doi.org/10.1145/1985441.1985472

Published: 21 May 2011 Publication History

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Abstract

Predicting bug-fix time is useful in several areas of software evolution, such as predicting software quality or coordinating development effort during bug triaging. Prior work has proposed bug-fix time prediction models that use various bug report attributes (e.g., number of developers who participated in fixing the bug, bug severity, number of patches, bug-opener's reputation) for estimating the time it will take to fix a newly-reported bug. In this paper we take a step towards constructing more accurate and more general bug-fix time prediction models by showing how existing models fail to validate on large projects widely-used in bug studies. In particular, we used multivariate and univariate regression testing to test the prediction significance of existing models on 512,474 bug reports from five open source projects: Eclipse, Chrome and three products from the Mozilla project (Firefox, Seamonkey and Thunderbird). The results of our regression testing indicate that the predictive power of existing models is between 30% and 49% and that there is a need for more independent variables (attributes) when constructing a prediction model. Additionally, we found that, unlike in prior recent studies on commercial software, in the projects we examined there is no correlation between bug-fix likelihood, bug-opener's reputation and the time it takes to fix a bug. These findings indicate three open research problems: (1) assessing whether prioritizing bugs using bug-opener's reputation is beneficial, (2) identifying attributes which are effective in predicting bug-fix time, and (3) constructing bug-fix time prediction models which can be validated on multiple projects.

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Cited By

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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
https://dl.acm.org/doi/10.1145/3663533.3664041
Sen RSingh V(2024)Bug Prediction Techniques: Analysis and ReviewReliability Engineering for Industrial Processes10.1007/978-3-031-55048-5_9(137-143)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-55048-5_9
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Index Terms

Bug-fix time prediction models: can we do better?
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools
    1. Software configuration management and version control systems

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Information & Contributors

Information

Published In

MSR '11: Proceedings of the 8th Working Conference on Mining Software Repositories

May 2011

260 pages

ISBN:9781450305747

DOI:10.1145/1985441

General Chair:
Arie van Deursen
Delft University of Technology, The Netherlands
,
Program Chairs:
Tao Xie
North Carolina State University, USA
,
Thomas Zimmermann
Microsoft Research, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2011

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ICSE11

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SIGSOFT

ICSE11: International Conference on Software Engineering

May 21 - 22, 2011

HI, Waikiki, Honolulu, USA

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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
https://dl.acm.org/doi/10.1145/3663533.3664041
Sen RSingh V(2024)Bug Prediction Techniques: Analysis and ReviewReliability Engineering for Industrial Processes10.1007/978-3-031-55048-5_9(137-143)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-55048-5_9
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Piantadosi VScalabrino SSerebrenik ANovielli NOliveto R(2023)Do attention and memory explain the performance of software developers?Empirical Software Engineering10.1007/s10664-023-10316-928:5Online publication date: 26-Aug-2023
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