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Hybrid genetic algorithm for improving fault localization

Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these te...

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Main Authors: Mahamad Zakaria, Muhammad Luqman, Sharif, Khaironi Yatim, Abd Ghani, Abdul Azim, Koh, Tieng Wei, Zulzalil, Hazura
Format: Article
Language:English
Published: American Scientific Publishers 2018
Online Access:http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf
http://psasir.upm.edu.my/id/eprint/64692/
https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000003/art00012
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spelling my.upm.eprints.646922018-08-14T02:39:23Z http://psasir.upm.edu.my/id/eprint/64692/ Hybrid genetic algorithm for improving fault localization Mahamad Zakaria, Muhammad Luqman Sharif, Khaironi Yatim Abd Ghani, Abdul Azim Koh, Tieng Wei Zulzalil, Hazura Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these techniques are made based on the assumption that the faults are caused by a single fault. However, in a reality, one fault could also possibly caused by multiple faults. This requires a technique which is specifically designed to handle multiple faults. With this regards, application of mutation analysis to localize faults might generate a vast number of mutants. As a result, these will lead to difficulty in choosing important mutants that are capable of localizing faults. Therefore, there is a need for a technique which able to localize a fault effectively with less number of mutants generated. Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. Since both had their own advantage, we have combined both techniques to enhance multiple localization of software fault. The result of the experiment shows that our technique able to detect multiple faults in various java programs but there is a need for improvement especially in prioritize fault that occurs simultaneously. American Scientific Publishers 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf Mahamad Zakaria, Muhammad Luqman and Sharif, Khaironi Yatim and Abd Ghani, Abdul Azim and Koh, Tieng Wei and Zulzalil, Hazura (2018) Hybrid genetic algorithm for improving fault localization. Advanced Science Letters, 24 (3). pp. 1587-1590. ISSN 1936-6612; ESSN: 1936-7317 https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000003/art00012 10.1166/asl.2018.11115
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Finding faults in a program correctly is crucial in software maintenance. In this light, many techniques have been proposed such as program slicing, code coverage, program state and mutation analysis. While all these techniques give us good insight on fault localization, but it appears that these techniques are made based on the assumption that the faults are caused by a single fault. However, in a reality, one fault could also possibly caused by multiple faults. This requires a technique which is specifically designed to handle multiple faults. With this regards, application of mutation analysis to localize faults might generate a vast number of mutants. As a result, these will lead to difficulty in choosing important mutants that are capable of localizing faults. Therefore, there is a need for a technique which able to localize a fault effectively with less number of mutants generated. Genetic algorithm (GA) is well known in finding an optimal solution to a problem while local search is capable of removing duplication. Since both had their own advantage, we have combined both techniques to enhance multiple localization of software fault. The result of the experiment shows that our technique able to detect multiple faults in various java programs but there is a need for improvement especially in prioritize fault that occurs simultaneously.
format Article
author Mahamad Zakaria, Muhammad Luqman
Sharif, Khaironi Yatim
Abd Ghani, Abdul Azim
Koh, Tieng Wei
Zulzalil, Hazura
spellingShingle Mahamad Zakaria, Muhammad Luqman
Sharif, Khaironi Yatim
Abd Ghani, Abdul Azim
Koh, Tieng Wei
Zulzalil, Hazura
Hybrid genetic algorithm for improving fault localization
author_facet Mahamad Zakaria, Muhammad Luqman
Sharif, Khaironi Yatim
Abd Ghani, Abdul Azim
Koh, Tieng Wei
Zulzalil, Hazura
author_sort Mahamad Zakaria, Muhammad Luqman
title Hybrid genetic algorithm for improving fault localization
title_short Hybrid genetic algorithm for improving fault localization
title_full Hybrid genetic algorithm for improving fault localization
title_fullStr Hybrid genetic algorithm for improving fault localization
title_full_unstemmed Hybrid genetic algorithm for improving fault localization
title_sort hybrid genetic algorithm for improving fault localization
publisher American Scientific Publishers
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/64692/1/Hybrid%20genetic%20algorithm%20for%20improving%20fault%20localization.pdf
http://psasir.upm.edu.my/id/eprint/64692/
https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000003/art00012
_version_ 1643838097147822080
score 13.211869

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