research-article

Using dynamic and contextual features to predict issue lifetime in GitHub projects

Authors:

Dietmar PfahlAuthors Info & Claims

MSR '16: Proceedings of the 13th International Conference on Mining Software Repositories

Pages 291 - 302

https://doi.org/10.1145/2901739.2901751

Published: 14 May 2016 Publication History

Abstract

Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.

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Cited By

Liu ZShen YZhang ZGuo YSun H(2025)On the Prediction of Open Source Software Ecosystem Health Based on Time Series AnalysisComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2373-0_10(138-149)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2373-0_10
Latendresse JAbedu SAbdellatif AShihab E(2024)An Exploratory Study on Machine Learning Model ManagementACM Transactions on Software Engineering and Methodology10.1145/368884134:1(1-31)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3688841
Lustosa AMenzies T(2024)Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project HealthACM Transactions on Software Engineering and Methodology10.1145/363025233:3(1-22)Online publication date: 14-Mar-2024
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Index Terms

Using dynamic and contextual features to predict issue lifetime in GitHub projects
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
      1. Open source model
    2. Software post-development issues
      1. Maintaining software

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Published In

cover image ACM Conferences

MSR '16: Proceedings of the 13th International Conference on Mining Software Repositories

May 2016

544 pages

ISBN:9781450341868

DOI:10.1145/2901739

General Chair:
Miryung Kim
University of California, Los Angeles
,
Program Chairs:
Romain Robbes
University of Chile, Chile
,
Christian Bird
Microsoft Research

Copyright © 2016 ACM.

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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IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Cited By

Liu ZShen YZhang ZGuo YSun H(2025)On the Prediction of Open Source Software Ecosystem Health Based on Time Series AnalysisComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2373-0_10(138-149)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2373-0_10
Latendresse JAbedu SAbdellatif AShihab E(2024)An Exploratory Study on Machine Learning Model ManagementACM Transactions on Software Engineering and Methodology10.1145/368884134:1(1-31)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3688841
Lustosa AMenzies T(2024)Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project HealthACM Transactions on Software Engineering and Methodology10.1145/363025233:3(1-22)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3630252
Bai SLiu HDai ELiu L(2024)Improving Issue-PR Link Prediction via Knowledge-Aware Heterogeneous Graph LearningIEEE Transactions on Software Engineering10.1109/TSE.2024.340844850:7(1901-1920)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3408448
Zhou YSun YWu JZhang JHuang G(2024)Wiki2GH: A Recommendation Service to Link Software Engineering Knowledge to Practical DevelopmentService Science10.1007/978-981-97-5760-2_14(203-220)Online publication date: 19-Aug-2024
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Yalçıner ADikici AGökalp E(2024)Data-Driven Software Engineering: A Systematic Literature ReviewSystems, Software and Services Process Improvement10.1007/978-3-031-71139-8_2(19-32)Online publication date: 7-Sep-2024
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Qiao YLu XWang CWang JTang WLi B(2024)Predicting Issue Resolution Time of OSS Using Multiple FeaturesJournal of Software: Evolution and Process10.1002/smr.274637:1Online publication date: 22-Nov-2024
https://doi.org/10.1002/smr.2746
Shafiq SMayr‐Dorn CMashkoor AEgyed A(2024)Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software developmentJournal of Software: Evolution and Process10.1002/smr.2655Online publication date: 13-Feb-2024
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Bugayenko YBakare ACheverda AFarina MKruglov APlaksin YPedrycz WSucci G(2023)Prioritizing tasks in software development: A systematic literature reviewPLOS ONE10.1371/journal.pone.028383818:4(e0283838)Online publication date: 6-Apr-2023
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Di Sorbo AZampetti FVisaggio ADi Penta MPanichella S(2023)Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software PlatformsACM Transactions on Software Engineering and Methodology10.1145/356482132:3(1-37)Online publication date: 26-Apr-2023
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