A Novel Ensemble Feature Selection and Software Defect Detection Model on Promise Defect Datasets
E. Sreedevi1, Y. Prasanth2
1E. Sreedevi, Research Scholar, Departement of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India.
2Dr. Y. Prasanth, Research Supervisor, Departement of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 May 2019 | Manuscript published on 30 May 2019 | PP: 3131-3136 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1468058119/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The prediction of software defects is an essential step before building high quality software. Although much research has been done for analyzing the software metrics and feature extraction. Unfortunately, traditional models failed to predict the defects using the multiple software projects data. As the number of software projects and modules increases, the sparsity and uncertainty of the data increases, which affects the overall true positive rate of the defect prediction process. In this paper, a hybrid ensemble feature selection and defect prediction model was designed and implemented on the openscience software defect dataset. ReliefF, Chi-square and improved predictive correlation measures are used in our ensemble feature selection process. Experimental results show that proposed model has high defect detection rate, recall and F-measure compared to the traditional software defect prediction models.
Index Terms: Machine Learning, Defect Detection, Decision Tree.
Scope of the Article: Machine Learning