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Issue No.09 - Sept. (2014 vol.40)
pp: 841-861
Wael Kessentini , Dept. of Comput. Sci., Univ. of Montreal, Montreal, QC, Canada
Marouane Kessentini , Dept. of Comput. Sci., Univ. of Michigan, Dearborn, MI, USA
Houari Sahraoui , Dept. of Comput. Sci., Univ. of Montreal, Montreal, QC, Canada
Slim Bechikh , Dept. of Comput. Sci., Univ. of Michigan, Dearborn, MI, USA
Ali Ouni , Dept. of Comput. Sci., Univ. of Michigan, Dearborn, MI, USA
ABSTRACT
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.
INDEX TERMS
Measurement, Sociology, Statistics, Evolutionary computation, Detectors, Optimization, Computational modeling,
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, vol.40, no. 9, pp. 841-861, Sept. 2014, doi:10.1109/TSE.2014.2331057
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