research-article

Defect prediction using social network analysis on issue repositories

Authors:

Ayşe Başar Bener,

Bora ÇağlayanAuthors Info & Claims

ICSSP '11: Proceedings of the 2011 International Conference on Software and Systems Process

Pages 63 - 71

https://doi.org/10.1145/1987875.1987888

Published: 21 May 2011 Publication History

Abstract

People are the most important pillar of software development process. It is critical to understand how they interact with each other and how these interactions affect the quality of the end product in terms of defects. In this research we propose to include a new set of metrics, a.k.a. social network metrics on issue repositories in predicting defects. Social network metrics on issue repositories has not been used before to predict defect proneness of a software product. To validate our hypotheses we used two datasets, development data of IBM¹ Rational ® Team Concert™ (RTC) and Drupal, to conduct our experiments. The results of the experiments revealed that compared to other set of metrics such as churn metrics using social network metrics on issue repositories either considerably decreases high false alarm rates without compromising the detection rates or considerably increases low prediction rates without compromising low false alarm rates. Therefore we recommend practitioners to collect social network metrics on issue repositories since people related information is a strong indicator of past patterns in a given team.

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Cited By

Schreiber R(2023)Organizational Influencers in Open-Source Software ProjectsInternational Journal of Open Source Software and Processes10.4018/IJOSSP.31840014:1(1-20)Online publication date: 16-Feb-2023
https://dl.acm.org/doi/10.4018/IJOSSP.318400
Santos FPenney JPimentel JWiese ISteinmacher IGerosa M(2023)Tell Me Who Are You Talking to and I Will Tell You What Issues Need Your Skills2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00087(611-623)Online publication date: May-2023
https://doi.org/10.1109/MSR59073.2023.00087
Mehmood IShahid SHussain HKhan IAhmad SRahman SUllah NHuda S(2023)A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine LearningIEEE Access10.1109/ACCESS.2023.328732611(63579-63597)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3287326
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Index Terms

Defect prediction using social network analysis on issue repositories
1. 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

ICSSP '11: Proceedings of the 2011 International Conference on Software and Systems Process

May 2011

256 pages

ISBN:9781450307307

DOI:10.1145/1987875

General Chair:
David Raffo
Portland State University, USA
,
Program Chairs:
Dietmar Pfahl
Lund University, Sweden
,
Li Zhang
BeiHang University, China

Copyright © 2011 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

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Publication History

Published: 21 May 2011

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ICSSP '11

ICSSP '11: International Conference on Software and Systems Process

May 21 - 22, 2011

HI, Waikiki, Honolulu, USA

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Cited By

Schreiber R(2023)Organizational Influencers in Open-Source Software ProjectsInternational Journal of Open Source Software and Processes10.4018/IJOSSP.31840014:1(1-20)Online publication date: 16-Feb-2023
Santos FPenney JPimentel JWiese ISteinmacher IGerosa M(2023)Tell Me Who Are You Talking to and I Will Tell You What Issues Need Your Skills2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00087(611-623)Online publication date: May-2023
Mehmood IShahid SHussain HKhan IAhmad SRahman SUllah NHuda S(2023)A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine LearningIEEE Access10.1109/ACCESS.2023.328732611(63579-63597)Online publication date: 2023
Komalasari ACandra M(2022)Improving Defect Prediction Using Combination of Software Metrics2022 International Conference on Data and Software Engineering (ICoDSE)10.1109/ICoDSE56892.2022.9971813(89-94)Online publication date: 2-Nov-2022
Sepahvand RAkbari RHashemi S(2020)Predicting the bug fixing time using word embedding and deep long short term memoriesIET Software10.1049/iet-sen.2019.026014:3(203-212)Online publication date: Jun-2020
Wiese IR RSteinmacher IKuroda ROliva GTreude CGerosa M(2017)Using contextual information to predict co-changesJournal of Systems and Software10.1016/j.jss.2016.07.016128:C(220-235)Online publication date: 1-Jun-2017
Cunha PFerreira ACortez P(2017)Mining Rational Team Concert Repositories: A Case Study on a Software ProjectProgress in Artificial Intelligence10.1007/978-3-319-65340-2_44(537-548)Online publication date: 9-Aug-2017
Bird CMenzies TZimmermann T(2015)The Art and Science of Analyzing Software DataundefinedOnline publication date: 15-Sep-2015
Miranskyy ACaglayan BBener ACialini EMorisio MDybå TTorchiano M(2014)Effect of temporal collaboration network, maintenance activity, and experience on defect exposureProceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/2652524.2652586(1-8)Online publication date: 18-Sep-2014
Wiese ICôgo FRé RSteinmacher IGerosa MWagner SDi Penta M(2014)Social metrics included in prediction models on software engineeringProceedings of the 10th International Conference on Predictive Models in Software Engineering10.1145/2639490.2639505(72-81)Online publication date: 17-Sep-2014
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