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Empirical studies on feature selection for software fault prediction

Authors:

Jiaqiang Chen,

Shulong Liu,

Xiang Chen,

Qing Gu,

Daoxu ChenAuthors Info & Claims

Internetware '13: Proceedings of the 5th Asia-Pacific Symposium on Internetware

Article No.: 26, Pages 1 - 4

https://doi.org/10.1145/2532443.2532461

Published: 23 October 2013 Publication History

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Abstract

Classification based software fault prediction methods aim to classify the modules into either fault-prone or non-fault-prone. Feature selection is a preprocess step used to improve the data quality. However most of previous research mainly focus on feature relevance analysis, there is little work focusing on feature redundancy analysis. Therefore we propose a two-stage framework for feature selection to solve this issue. In particular, during the feature relevance phase, we adopt three different relevance measures to obtain the relevant feature subset. Then during the feature redundancy analysis phase, we use a cluster-based method to eliminate redundant features. To verify the effectiveness of our proposed framework, we choose typical real-world software projects, including Eclipse projects and NASA software project KC1. Final empirical result shows the effectiveness of our proposed framework.

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Cited By

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Bhandari KKumar KSangal A(2022)Data quality issues in software fault prediction: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-022-10371-656:8(7839-7908)Online publication date: 21-Dec-2022
https://doi.org/10.1007/s10462-022-10371-6
Juneja K(2019)A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluationApplied Soft Computing10.1016/j.asoc.2019.02.00877(696-713)Online publication date: Apr-2019
https://doi.org/10.1016/j.asoc.2019.02.008
Pandey SMishra RTriphathi A(2018)Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive ModelProcedia Computer Science10.1016/j.procs.2018.05.071132(1412-1421)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.05.071
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Empirical studies on feature selection for software fault prediction

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Information & Contributors

Information

Published In

Internetware '13: Proceedings of the 5th Asia-Pacific Symposium on Internetware

October 2013

211 pages

ISBN:9781450323697

DOI:10.1145/2532443

Conference Chairs:
Hong Mei,
Jian Lv,
Program Chair:
Xiaoguang Mao

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: 23 October 2013

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Research-article

Funding Sources

University Natural Science Research Project of Jiangsu Province
Nantong Application Research Plan
National Natural Science Foundation of China
Open Project of State Key Laboratory for Novel Software Technology at Nanjing University

Conference

Internetware '13

Sponsor:

Internetware '13: The Fifth Asia-Pacific Symposium on Internetware

October 23 - 24, 2013

Changsha, China

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Internetware '13 Paper Acceptance Rate 15 of 50 submissions, 30%;

Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

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Bhandari KKumar KSangal A(2022)Data quality issues in software fault prediction: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-022-10371-656:8(7839-7908)Online publication date: 21-Dec-2022
https://doi.org/10.1007/s10462-022-10371-6
Juneja K(2019)A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluationApplied Soft Computing10.1016/j.asoc.2019.02.00877(696-713)Online publication date: Apr-2019
https://doi.org/10.1016/j.asoc.2019.02.008
Pandey SMishra RTriphathi A(2018)Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive ModelProcedia Computer Science10.1016/j.procs.2018.05.071132(1412-1421)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.05.071
Kaur RSharma S(2018)An ANN Based Approach for Software Fault Prediction Using Object Oriented MetricsAdvanced Informatics for Computing Research10.1007/978-981-13-3140-4_31(341-354)Online publication date: 12-Dec-2018
https://doi.org/10.1007/978-981-13-3140-4_31
Liu WLiu SGu QChen JChen XChen D(2016)Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault PredictionIEEE Transactions on Reliability10.1109/TR.2015.246167665:1(38-53)Online publication date: Mar-2016
https://doi.org/10.1109/TR.2015.2461676
Lee SLi DLi Y(2016)An Investigation of Essential Topics on Software Fault-Proneness Prediction2016 International Symposium on System and Software Reliability (ISSSR)10.1109/ISSSR.2016.016(37-46)Online publication date: Oct-2016
https://doi.org/10.1109/ISSSR.2016.016
Erturk EAkcapinar Sezer E(2016)Iterative software fault prediction with a hybrid approachApplied Soft Computing10.1016/j.asoc.2016.08.02549:C(1020-1033)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.025
Chen JLiu SLiu WChen XGu QChen D(2014)A Two-Stage Data Preprocessing Approach for Software Fault Prediction2014 Eighth International Conference on Software Security and Reliability10.1109/SERE.2014.15(20-29)Online publication date: Jun-2014
https://doi.org/10.1109/SERE.2014.15

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