research-article

Using the gini coefficient for bug prediction in eclipse

Authors:

Martin Pinzger,

Harald GallAuthors Info & Claims

IWPSE-EVOL '11: Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution

Pages 51 - 55

https://doi.org/10.1145/2024445.2024455

Published: 05 September 2011 Publication History

Abstract

The Gini coefficient is a prominent measure to quantify the inequality of a distribution. It is often used in the field of economy to describe how goods, e.g., wealth or farmland, are distributed among people. We use the Gini coefficient to measure code ownership by investigating how changes made to source code are distributed among the developer population. The results of our study with data from the Eclipse platform show that less bugs can be expected if a large share of all changes are accumulated, i.e., carried out, by relatively few developers.

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Cited By

Kim CGoh SChoi MLee KChoi M(2020)Hub-Periphery Hierarchy in Bus Transportation Networks: Gini Coefficients and the Seoul Bus SystemSustainability10.3390/su1218729712:18(7297)Online publication date: 6-Sep-2020
https://doi.org/10.3390/su12187297
Wen MWu RCheung S(2020)How Well Do Change Sequences Predict Defects? Sequence Learning from Software ChangesIEEE Transactions on Software Engineering10.1109/TSE.2018.287625646:11(1155-1175)Online publication date: 1-Nov-2020
https://doi.org/10.1109/TSE.2018.2876256
Decan AMens TGrosjean P(2019)An empirical comparison of dependency network evolution in seven software packaging ecosystemsEmpirical Software Engineering10.1007/s10664-017-9589-y24:1(381-416)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10664-017-9589-y
Show More Cited By

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Using the gini coefficient for bug prediction in eclipse

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cover image ACM Conferences

IWPSE-EVOL '11: Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution

September 2011

140 pages

ISBN:9781450308489

DOI:10.1145/2024445

Program Chairs:
Anthony Cleve
University of Namur, Belgium
,
Romain Robbes
University of Chile, Chile

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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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

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Published: 05 September 2011

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ESEC/FSE'11

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SIGSOFT

ESEC/FSE'11: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

September 5 - 6, 2011

Szeged, Hungary

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Cited By

Kim CGoh SChoi MLee KChoi M(2020)Hub-Periphery Hierarchy in Bus Transportation Networks: Gini Coefficients and the Seoul Bus SystemSustainability10.3390/su1218729712:18(7297)Online publication date: 6-Sep-2020
https://doi.org/10.3390/su12187297
Wen MWu RCheung S(2020)How Well Do Change Sequences Predict Defects? Sequence Learning from Software ChangesIEEE Transactions on Software Engineering10.1109/TSE.2018.287625646:11(1155-1175)Online publication date: 1-Nov-2020
https://doi.org/10.1109/TSE.2018.2876256
Decan AMens TGrosjean P(2019)An empirical comparison of dependency network evolution in seven software packaging ecosystemsEmpirical Software Engineering10.1007/s10664-017-9589-y24:1(381-416)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10664-017-9589-y
Zhang FHassan AMcIntosh SZou Y(2017)The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction ModelsIEEE Transactions on Software Engineering10.1109/TSE.2016.259916143:5(476-491)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/TSE.2016.2599161
Mens T(2016)An Ecosystemic and Socio-Technical View on Software Maintenance and Evolution2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2016.19(1-8)Online publication date: Oct-2016
https://doi.org/10.1109/ICSME.2016.19
Madeyski LJureczko M(2015)Which process metrics can significantly improve defect prediction models? An empirical studySoftware Quality Journal10.1007/s11219-014-9241-723:3(393-422)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11219-014-9241-7
Lu HKocaguneli ECukic B(2014)Defect Prediction between Software Versions with Active Learning and Dimensionality ReductionProceedings of the 2014 IEEE 25th International Symposium on Software Reliability Engineering10.1109/ISSRE.2014.35(312-322)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1109/ISSRE.2014.35
Ghezzi GGall HZimmermann TDi Penta MKim S(2013)Replicating mining studies with SOFASProceedings of the 10th Working Conference on Mining Software Repositories10.5555/2487085.2487152(363-372)Online publication date: 18-May-2013
https://dl.acm.org/doi/10.5555/2487085.2487152
Ghezzi GGall H(2013)Replicating mining studies with SOFAS2013 10th Working Conference on Mining Software Repositories (MSR)10.1109/MSR.2013.6624050(363-372)Online publication date: May-2013
https://doi.org/10.1109/MSR.2013.6624050
Goloshchapova OLumpe M(2013)On the Application of Inequality Indices in Comparative Software AnalysisProceedings of the 2013 22nd Australian Conference on Software Engineering10.1109/ASWEC.2013.23(117-126)Online publication date: 4-Jun-2013
https://dl.acm.org/doi/10.1109/ASWEC.2013.23
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