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A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection
PrePrint
ISSN: 0098-5589
Wael Kessentini, Wael Kassentini is with the University of Montreal, Canada. (e-mail: waelk@umontreal.ca).
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 9 large open source systems where more than 85% of precision and recall scores are obtained on a variety of 8 different types of code-smells.
Citation:
Wael Kessentini, Marouane Kessentini, Houari Sahraoui, Slim Bechikh, Ali Ouni, "A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection," IEEE Transactions on Software Engineering, 23 June 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TSE.2014.2331057>
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