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A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction

Authors:

Witold Pedrycz,

Giancarlo SucciAuthors Info & Claims

ICSE '08: Proceedings of the 30th international conference on Software engineering

Pages 181 - 190

https://doi.org/10.1145/1368088.1368114

Published: 10 May 2008 Publication History

Abstract

In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.

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Cited By

Ahmad SChowdhury SHolmes R(2025)Impact of methodological choices on the analysis of code metrics and maintenanceJournal of Systems and Software10.1016/j.jss.2024.112263220(112263)Online publication date: Feb-2025
https://doi.org/10.1016/j.jss.2024.112263
Chouchen MOuni AMkaouer M(2024)MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective SearchACM Transactions on Software Engineering and Methodology10.1145/368047233:8(1-44)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3680472
Jiang MJiang JWu TMa ZLuo XZhou Y(2024)Understanding Vulnerability Inducing Commits of the Linux KernelACM Transactions on Software Engineering and Methodology10.1145/367245233:7(1-28)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672452
Show More Cited By

Index Terms

A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. System management
        Quality assurance
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines
    2. Software verification and validation
      1. Process validation

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Published In

cover image ACM Conferences

ICSE '08: Proceedings of the 30th international conference on Software engineering

May 2008

558 pages

ISBN:9781605580791

DOI:10.1145/1368088

General Chair:
Wilhelm Schäfer
University of Paderborn
,
Program Chairs:
Matthew B. Dwyer
University of Nebraska
,
Volker Gruhn
University of Leipzig

Copyright © 2008 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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Published: 10 May 2008

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ICSE '08

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ICSE '08: International Conference on Software Engineering

May 10 - 18, 2008

Leipzig, Germany

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ICSE '08 Paper Acceptance Rate 56 of 370 submissions, 15%;

Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Ahmad SChowdhury SHolmes R(2025)Impact of methodological choices on the analysis of code metrics and maintenanceJournal of Systems and Software10.1016/j.jss.2024.112263220(112263)Online publication date: Feb-2025
https://doi.org/10.1016/j.jss.2024.112263
Chouchen MOuni AMkaouer M(2024)MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective SearchACM Transactions on Software Engineering and Methodology10.1145/368047233:8(1-44)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3680472
Jiang MJiang JWu TMa ZLuo XZhou Y(2024)Understanding Vulnerability Inducing Commits of the Linux KernelACM Transactions on Software Engineering and Methodology10.1145/367245233:7(1-28)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672452
Abdu AZhai ZAbdo HAlgabri R(2024)Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic GraphIEEE Transactions on Reliability10.1109/TR.2024.335496573:2(820-834)Online publication date: Jun-2024
https://doi.org/10.1109/TR.2024.3354965
Su WHuang C(2024)Application of Weighted Combinations of Activation Functions to Defect Prediction in Software DevelopmentIEEE Transactions on Reliability10.1109/TR.2023.328485773:1(680-694)Online publication date: Mar-2024
https://doi.org/10.1109/TR.2023.3284857
Morita IKashiwa YKondo MSohn JMcIntosh SKamei YUbayashi N(2024)TraceJIT: Evaluating the Impact of Behavioral Code Change on Just-In-Time Defect Prediction2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00065(580-591)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00065
Chen ZHuang CLin JFang CChu W(2024)Employing CNN with Spatial Pyramid Pooling for Predicting Software Defects through Image Analysis2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS62785.2024.00039(318-327)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS62785.2024.00039
Green CJaspan CHodges MLin J(2024)Developer Productivity for Humans, Part 7: Software QualityIEEE Software10.1109/MS.2023.332483041:1(25-30)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/MS.2023.3324830
Haldar SCapretz L(2024)Feature Importance in the Context of Traditional and Just-In-Time Software Defect Prediction Models2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE59415.2024.10667167(818-822)Online publication date: 6-Aug-2024
https://doi.org/10.1109/CCECE59415.2024.10667167
Jász J(2024)The Effectiveness of Hidden Dependence Metrics in Bug PredictionIEEE Access10.1109/ACCESS.2024.340692912(77214-77225)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3406929
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