research-article

On the influence of maintenance activity types on the issue resolution time

Authors:

Alessandro Murgia,

Roberto Tonelli,

Michele MarchesiAuthors Info & Claims

PROMISE '14: Proceedings of the 10th International Conference on Predictive Models in Software Engineering

Pages 12 - 21

https://doi.org/10.1145/2639490.2639506

Published: 17 September 2014 Publication History

Abstract

The ISO/IEC 14764 standard specifies four types of software maintenance activities spanning the different motivations that software engineers have while performing changes to an existing software system. Undoubtedly, this classification has helped in organizing the workflow within software projects, however for planning purposes the relative time differences for the respective tasks remains largely unexplored.

In this empirical study, we investigate the influence of the maintenance type on issue resolution time. From GitHub's issue repository, we analyze more than 14000 issue reports taken from 34 open source projects and classify them as corrective, adaptive, perfective or preventive maintenance. Based on this data, we show that the issue resolution time depends on the maintenance type. Moreover, we propose a statistical model to describe the distribution of the issue resolution time for each type of maintenance activity. Finally, we demonstrate the usefulness of this model for scheduling the maintenance workload.

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Cited By

Lai TSimmons ABarnett SSchneider JVasa R(2024)Comparative analysis of real issues in open-source machine learning projectsEmpirical Software Engineering10.1007/s10664-024-10467-329:3Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1007/s10664-024-10467-3
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
https://doi.org/10.1371/journal.pone.0283838
Bai SLiu LMeng CLiu H(2023)Automating discussion structure re-organization for GitHub issuesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120024225:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120024
Show More Cited By

Index Terms

On the influence of maintenance activity types on the issue resolution time
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation

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cover image ACM Other conferences

PROMISE '14: Proceedings of the 10th International Conference on Predictive Models in Software Engineering

September 2014

98 pages

ISBN:9781450328982

DOI:10.1145/2639490

General Chair:
Stefan Wagner
University of Stuttgart, Germany
,
Program Chair:
Massimiliano Di Penta
University of Sannio, Italy

Copyright © 2014 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2014

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Agentschap voor Innovatie door Wetenschap en Technologie
Regione Autonoma della Sardegna (RAS)

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PROMISE '14

PROMISE '14: The 10th International Conference on Predictive Models in Software Engineering

September 17, 2014

Turin, Italy

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PROMISE '14 Paper Acceptance Rate 9 of 21 submissions, 43%;

Overall Acceptance Rate 98 of 213 submissions, 46%

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Cited By

Lai TSimmons ABarnett SSchneider JVasa R(2024)Comparative analysis of real issues in open-source machine learning projectsEmpirical Software Engineering10.1007/s10664-024-10467-329:3Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1007/s10664-024-10467-3
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
https://doi.org/10.1371/journal.pone.0283838
Bai SLiu LMeng CLiu H(2023)Automating discussion structure re-organization for GitHub issuesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120024225:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120024
Seker ADiri BArslan HAmasyalı M(2022)Open Source Software Development ChallengesResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch102(2134-2164)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch102
Popoola SZhao XGray JGarcia-Dominguez AKühn TSousa V(2022)Classifying changes to models via changeset metricsProceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3550356.3561563(276-285)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1145/3550356.3561563
Yimer SMolla YAlemneh E(2022)Predicting Software Maintenance Type, Change Impact, and Maintenance Time Using Machine Learning Algorithms2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)10.1109/ICT4DA56482.2022.9971350(37-41)Online publication date: 28-Nov-2022
https://doi.org/10.1109/ICT4DA56482.2022.9971350
Seker ADiri BArslan HAmasyalı M(2021)Open Source Software Development ChallengesResearch Anthology on Usage and Development of Open Source Software10.4018/978-1-7998-9158-1.ch003(33-62)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9158-1.ch003
Şeker ADiri BArslan H(2021)New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation SystemsApplied Sciences10.3390/app1103092011:3(920)Online publication date: 20-Jan-2021
https://doi.org/10.3390/app11030920
Lafia SThomer ABleckley DAkmon DHemphill L(2021)Leveraging Machine Learning to Detect Data Curation Activities2021 IEEE 17th International Conference on eScience (eScience)10.1109/eScience51609.2021.00025(149-158)Online publication date: Sep-2021
https://doi.org/10.1109/eScience51609.2021.00025
Popoola SGray J(2021)Artifact Analysis of Smell Evolution and Maintenance Tasks in Simulink Models2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C53483.2021.00128(817-826)Online publication date: Oct-2021
https://doi.org/10.1109/MODELS-C53483.2021.00128
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