An Artificial Neural Network Genetic Algorithm with Shuffled Frog Leap Algorithm for Software Defect Prediction
S.V. Achuta Rao1, P. Santosh Kumar Patra2
1Dr. S.V. Achuta Rao, Professor, Department of IT, St. Martin’s Engineering College, Dhulapally, Secunderabad (Telangana), India.
2Dr. P. Santosh Kumar Patra, Principal & Professor, Department of CSE, St. Martin’s Engineering College, Dhulapally, Secunderabad (Telangana), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 26 December 2019 | PP: 824-829 | Volume-8 Issue-12S October 2019 | Retrieval Number: L118710812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1187.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Defect prediction performances are significant to attain quality of the software and to understand previous errors. In this work, for assessing the classification accuracy, precision, and recall and F measure for various classifiers are used. The artificial neural network optimizations make the assumption that more than two algorithms for one optimization have been implemented. The optimization makes use of a heuristic for choosing the best of the algorithms for being applied in a particular situation. An approach of hybrid optimization for designing of the linkages method and is used for the dimensional synthesis of the mechanism. The ANN models are assisted in their convergence towards a global minimum by the multi-directional search algorithm that is incorporated in the GA. The results have shown an accuracy of classification of the NN-hybrid shuffled from algorithm to perform better by about 5.94% than that of the fuzzy classifiers and by about 3.59% of the NN-Lm training and by about 1.42% of the NN-shuffled frog algorithm.
Keywords: Hybrid Optimization, Fuzzy Classifiers, Defect Prediction, Classification and Hybrid Shuffled Frog Leap.
Scope of the Article: Software Engineering Methodologies