Optimizing selection of competing features via feedback-directed evolutionary algorithms

Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Softw...

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Main Authors: TAN, Tian Huat, XUE, Yinxing, CHEN, Manman, SUN, Jun, LIU, Yang, DONG, Jin Song Dong
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/4954
https://ink.library.smu.edu.sg/context/sis_research/article/5957/viewcontent/2771783.2771808.pdf
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spelling sg-smu-ink.sis_research-59572020-02-27T03:15:05Z Optimizing selection of competing features via feedback-directed evolutionary algorithms TAN, Tian Huat XUE, Yinxing CHEN, Manman SUN, Jun LIU, Yang DONG, Jin Song Dong Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In this work, we incorporated a novel feedback-directed mechanism into existing EAs. Our empirical results have shown that our method has improved noticeably over all unguided version of EAs on the optimal feature selection. In particular, for case studies in SPLOT and LVAT repositories, the feedback-directed Indicator-Based EA (IBEA) has increased the number of correct solutions found by 72.33% and 75%, compared to unguided IBEA. In addition, by leveraging a pre-computed solution, we have found 34 sound solutions for Linux X86, which contains 6888 features, in less than 40 seconds. 2015-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4954 info:doi/10.1145/2771783.2771808 https://ink.library.smu.edu.sg/context/sis_research/article/5957/viewcontent/2771783.2771808.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software product line evolutionary algorithms SAT solvers Software Engineering Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software product line
evolutionary algorithms
SAT solvers
Software Engineering
Theory and Algorithms
spellingShingle Software product line
evolutionary algorithms
SAT solvers
Software Engineering
Theory and Algorithms
TAN, Tian Huat
XUE, Yinxing
CHEN, Manman
SUN, Jun
LIU, Yang
DONG, Jin Song Dong
Optimizing selection of competing features via feedback-directed evolutionary algorithms
description Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In this work, we incorporated a novel feedback-directed mechanism into existing EAs. Our empirical results have shown that our method has improved noticeably over all unguided version of EAs on the optimal feature selection. In particular, for case studies in SPLOT and LVAT repositories, the feedback-directed Indicator-Based EA (IBEA) has increased the number of correct solutions found by 72.33% and 75%, compared to unguided IBEA. In addition, by leveraging a pre-computed solution, we have found 34 sound solutions for Linux X86, which contains 6888 features, in less than 40 seconds.
format text
author TAN, Tian Huat
XUE, Yinxing
CHEN, Manman
SUN, Jun
LIU, Yang
DONG, Jin Song Dong
author_facet TAN, Tian Huat
XUE, Yinxing
CHEN, Manman
SUN, Jun
LIU, Yang
DONG, Jin Song Dong
author_sort TAN, Tian Huat
title Optimizing selection of competing features via feedback-directed evolutionary algorithms
title_short Optimizing selection of competing features via feedback-directed evolutionary algorithms
title_full Optimizing selection of competing features via feedback-directed evolutionary algorithms
title_fullStr Optimizing selection of competing features via feedback-directed evolutionary algorithms
title_full_unstemmed Optimizing selection of competing features via feedback-directed evolutionary algorithms
title_sort optimizing selection of competing features via feedback-directed evolutionary algorithms
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/4954
https://ink.library.smu.edu.sg/context/sis_research/article/5957/viewcontent/2771783.2771808.pdf
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