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A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection.
- Source :
-
IEEE Transactions on Software Engineering . Sep2014, Vol. 40 Issue 9, p841-861. 21p. - Publication Year :
- 2014
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00985589
- Volume :
- 40
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Software Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 109554564
- Full Text :
- https://doi.org/10.1109/TSE.2014.2331057