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A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection

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5 Author(s)
Kessentini, W. ; Department of Computer Science, University of Montreal, Montreal, Quebec, Canada ; Kessentini, M. ; Sahraoui, H. ; Bechikh, S.
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We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary algorithms with different adaptations (fitness functions, solution representations, and change operators) are executed, in a parallel cooperative manner, to solve a common goal which is the detection of code-smells. An empirical evaluation to compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and two code-smells detection techniques that are not based on meta-heuristics search. The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells.

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Published in:

Software Engineering, IEEE Transactions on  (Volume:40 ,  Issue: 9 )

Date of Publication:

Sept. 1 2014

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