Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2015

Abstract

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.

Keywords

Software product line, evolutionary algorithms, SAT solvers

Discipline

Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2015 International Symposium on Software Testing and Analysis, Baltimore, USA, July 13-17

First Page

246

Last Page

256

ISBN

9781450336208

Identifier

10.1145/2771783.2771808

Publisher

ACM

City or Country

Baltimore, USA

Additional URL

https://doi.org/10.1145/2771783.2771808

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