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An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 9962)

Abstract

SBSE researchers often use an evolutionary algorithm to solve various software engineering problems. This paper explores an alternate approach of sampling. This approach is called SWAY (Samplying WAY) and finds the (near) optimal solutions to the problem by (i) creating a larger initial population and (ii) intelligently sampling the solution space to find the best subspace. Unlike evolutionary algorithms, SWAY does not use mutation or cross-over or multi-generational reasoning to find interesting subspaces but relies on the underlying dimensions of the solution space. Experiments with Software Engineering (SE) models shows that SWAY’s performance improvement is competitive with standard MOEAs while, terminating over an order of magnitude faster.

Keywords

  • Search-based SE
  • Sampling
  • Evolutionary algorithms

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Notes

  1. 1.

    We use these NSGA-II and SPEA2 since, in his survey of the SSBE literature in the period 2004 to 2013, Sayyad [24] found 25 different algorithms. Of those, NSGA-II [7] or SPEA2 [32] were used four times as often as anything else. For comments on newer algorithms (NSGA-III and MOEA/D) see our Future Work section.

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Correspondence to Vivek Nair .

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Nair, V., Menzies, T., Chen, J. (2016). An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-47106-8_7

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