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High dimensional search-based software engineering: finding tradeoffs among 15 objectives for automating software refactoring using NSGA-III

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conference contribution
posted on 2015-03-16, 18:22 authored by Wiem Mkaouer, Marouane Kessentini, Slim Bechikh, Kalyanmoy Deb, Mel Ó Cinnéide
There is a growing need for scalable search-based software engineering approaches that address software engineering problems where a large number of objectives are to be optimized. Software refactoring is one of these problems where a refactoring sequence is sought that optimizes several software metrics. Most of the existing refactoring work uses a large set of quality metrics to evaluate the software design after applying refactoring operations, but current search-based software engineering approaches are limited to using a maximum of five metrics. We propose for the first time a scalable search-based software engineering approach based on a newly proposed evolutionary optimization method NSGA-III where there are 15 different objectives to be optimized. In our approach, automated refactoring solutions are evaluated using a set of 15 distinct quality metrics. We evaluated this approach on seven large open source systems and found that, on average, more than 92% of code smells were corrected. Statistical analysis of our experiments over 31 runs shows that NSGA-III performed significantly better than two other many-objective techniques (IBEA and MOEA/D), a multi-objective algorithm (NSGA-II) and two mono-objective approaches, hence demonstrating that our NSGA-III approach represents the new state of the art in fully-automated refactoring.

History

Publication

GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation;pp. 1263-1270

Publisher

Association for Computing Machinery

Note

peer-reviewed

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SFI

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"© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation, pp. 1263-1270, http://dx.doi.org/10.1145/2576768.2598366

Language

English

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