research-article

Software Defect Prediction Using Feature Space Transformation

Authors:

Md. Habibur Rahman,

Sheikh Muhammad Sarwar,

Mohammad ShoyaibAuthors Info & Claims

ICC '16: Proceedings of the International Conference on Internet of things and Cloud Computing

Article No.: 72, Pages 1 - 6

https://doi.org/10.1145/2896387.2900324

Published: 22 March 2016 Publication History

Abstract

In software quality estimation research, software defect prediction is a key topic. A defect prediction model is generally constructed using a variety of software attributes and each attribute may have positive, negative or neutral effect on a specific model. Selection of an optimal set of attributes for model development remains a vital yet unexplored issue. In this paper, we have introduced a new feature space transformation process with a normalization technique to improve the defect prediction accuracy. We proposed a feature space transformation technique and classify the instances using Support Vector Machine (SVM) with its histogram intersection kernel. The proposed method is evaluated using the data sets from NASA metric data repository and its application demonstrates acceptable accuracy.

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Cited By

Yang LGuo XChen JWang YMa HLi YYang ZShi Y(2023)Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield MachineChinese Journal of Mechanical Engineering10.1186/s10033-023-00932-336:1Online publication date: 24-Oct-2023
https://doi.org/10.1186/s10033-023-00932-3
Ali MMazhar TShahzad TGhadi YMohsin SAkber SAli M(2023)Analysis of Feature Selection Methods in Software Defect Prediction ModelsIEEE Access10.1109/ACCESS.2023.334324911(145954-145974)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3343249
Juneja SNauman AUppal MGupta DAlroobaea RMuminov BTao Y(2023)Machine learning-based defect prediction model using multilayer perceptron algorithm for escalating the reliability of the softwareThe Journal of Supercomputing10.1007/s11227-023-05836-680:7(10122-10147)Online publication date: 19-Dec-2023
https://doi.org/10.1007/s11227-023-05836-6
Show More Cited By

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Published In

cover image ACM Other conferences

ICC '16: Proceedings of the International Conference on Internet of things and Cloud Computing

March 2016

535 pages

ISBN:9781450340632

DOI:10.1145/2896387

Editors:
Djallel Eddine Boubiche
University of Batna 2, Algeria
,
Faouzi Hidoussi
Hadj Lakhdar University, Algeria
,
Lyamine Guezouli
University of Batna 2, Algeria
,
Ahcène Bounceur
University of Western Britanny, France
,
Homero Toral Cruz
University of Quintana Roo, Mexico

Copyright © 2016 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

New York, NY, United States

Publication History

Published: 22 March 2016

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ICC '16

ICC '16: International Conference on Internet of things and Cloud Computing

March 22 - 23, 2016

Cambridge, United Kingdom

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Overall Acceptance Rate 213 of 590 submissions, 36%

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Cited By

Yang LGuo XChen JWang YMa HLi YYang ZShi Y(2023)Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield MachineChinese Journal of Mechanical Engineering10.1186/s10033-023-00932-336:1Online publication date: 24-Oct-2023
https://doi.org/10.1186/s10033-023-00932-3
Ali MMazhar TShahzad TGhadi YMohsin SAkber SAli M(2023)Analysis of Feature Selection Methods in Software Defect Prediction ModelsIEEE Access10.1109/ACCESS.2023.334324911(145954-145974)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3343249
Juneja SNauman AUppal MGupta DAlroobaea RMuminov BTao Y(2023)Machine learning-based defect prediction model using multilayer perceptron algorithm for escalating the reliability of the softwareThe Journal of Supercomputing10.1007/s11227-023-05836-680:7(10122-10147)Online publication date: 19-Dec-2023
https://doi.org/10.1007/s11227-023-05836-6
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
Hemandez-Molinos MSanchez-Garcia ABarrientos-Martinez R(2021)Classification Algorithms for Software Defect Prediction: A Systematic Literature Review2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT52520.2021.00034(189-196)Online publication date: Oct-2021
https://doi.org/10.1109/CONISOFT52520.2021.00034
Felix ELee S(2017)Integrated Approach to Software Defect PredictionIEEE Access10.1109/ACCESS.2017.27591805(21524-21547)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2759180

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