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Article

Improving defect prediction using temporal features and non linear models

Authors:

Abraham Bernstein,

Jayalath Ekanayake,

Martin PinzgerAuthors Info & Claims

IWPSE '07: Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting

Pages 11 - 18

https://doi.org/10.1145/1294948.1294953

Published: 03 September 2007 Publication History

Abstract

Predicting the defects in the next release of a large software system is a very valuable asset for the project manger to plan her resources. In this paper we argue that temporal features (or aspects) of the data are central to prediction performance. We also argue that the use of non-linear models, as opposed to traditional regression, is necessary to uncover some of the hidden interrelationships between the features and the defects and maintain the accuracy of the prediction in some cases.

Using data obtained from the CVS and Bugzilla repositories of the Eclipse project, we extract a number of temporal features, such as the number of revisions and number of reported issues within the last three months. We then use these data to predict both the location of defects (i.e., the classes in which defects will occur) as well as the number of reported bugs in the next month of the project. To that end we use standard tree-based induction algorithms in comparison with the traditional regression.

Our non-linear models uncover the hidden relationships between features and defects, and present them in easy to understand form. Results also show that using the temporal features our prediction model can predict whether a source file will have a defect with an accuracy of 99% (area under ROC curve 0.9251) and the number of defects with a mean absolute error of 0.019 (Spearman's correlation of 0.96).

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Cited By

Balasubramaniam BAhmed IBagheri HBradley J(2024)Carving out Control Code: Automated Identification of Control Software in Autopilot SystemsACM Transactions on Cyber-Physical Systems10.1145/3678259Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3678259
Feyzi FDaneshdoost A(2023)Studying the effectiveness of deep active learning in software defect predictionInternational Journal of Computers and Applications10.1080/1206212X.2023.225211745:7-8(534-552)Online publication date: 5-Sep-2023
https://doi.org/10.1080/1206212X.2023.2252117
Pandey STripathi A(2023)DBDNN-Estimator: A Cross-Project Number of Fault Estimation TechniqueSN Computer Science10.1007/s42979-023-02364-15:1Online publication date: 20-Nov-2023
https://doi.org/10.1007/s42979-023-02364-1
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Improving defect prediction using temporal features and non linear models

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Published In

cover image ACM Conferences

IWPSE '07: Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting

September 2007

122 pages

ISBN:9781595937223

DOI:10.1145/1294948

Program Chairs:
Massimiliano Di Penta
RCOST --- Università degli Studi del Sannio, Italy
,
Michele Lanza
University of Lugano, Switzerland

Copyright © 2007 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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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
CEPIS: The Council of European Professional Informatics Societies

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Association for Computing Machinery

New York, NY, United States

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Published: 03 September 2007

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ESEC/FSE07

Sponsor:

ACM
SIGSOFT
CEPIS

ESEC/FSE07: Joint 11th European Software Engineering Conference 2007

September 3 - 4, 2007

Dubrovnik, Croatia

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Cited By

Balasubramaniam BAhmed IBagheri HBradley J(2024)Carving out Control Code: Automated Identification of Control Software in Autopilot SystemsACM Transactions on Cyber-Physical Systems10.1145/3678259Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3678259
Feyzi FDaneshdoost A(2023)Studying the effectiveness of deep active learning in software defect predictionInternational Journal of Computers and Applications10.1080/1206212X.2023.225211745:7-8(534-552)Online publication date: 5-Sep-2023
https://doi.org/10.1080/1206212X.2023.2252117
Pandey STripathi A(2023)DBDNN-Estimator: A Cross-Project Number of Fault Estimation TechniqueSN Computer Science10.1007/s42979-023-02364-15:1Online publication date: 20-Nov-2023
https://doi.org/10.1007/s42979-023-02364-1
Wu YZhang YXu KWang TWang H(2022)Understanding and Predicting Docker Build Duration: An Empirical Study of Containerized Workflow of OSS ProjectsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556940(1-13)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556940
Gesi JLi JAhmed ILanubile F(2021)An Empirical Examination of the Impact of Bias on Just-in-time Defect PredictionProceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3475716.3475791(1-12)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3475716.3475791
Kaur IKaur A(2021)Comparative analysis of software fault prediction using various categories of classifiersInternational Journal of System Assurance Engineering and Management10.1007/s13198-021-01110-112:3(520-535)Online publication date: 10-May-2021
https://doi.org/10.1007/s13198-021-01110-1
Felix ELee S(2020)Predicting the number of defects in a new software versionPLOS ONE10.1371/journal.pone.022913115:3(e0229131)Online publication date: 18-Mar-2020
https://doi.org/10.1371/journal.pone.0229131
Brindescu CAhmed ILeano RSarma ARothermel GBae D(2020)Planning for untanglingProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380344(801-811)Online publication date: 27-Jun-2020
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Mannan UAhmed IJensen CSarma ADevanbu PCohen MZimmermann T(2020)On the relationship between design discussions and design quality: a case study of Apache projectsProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409707(543-555)Online publication date: 8-Nov-2020
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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
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