Software Defect Prediction Analysis by using Machine Learning Algorithms.
Naveen Babu1, Himagiri2, V Vamshi Krishna3, A Anil Kumar4, M  Ravi5

1Naveen Babu, Department of CSE, JBIET, Himagiri, Hyderabad (Telangana), India.
2V Vamshi Krishna, PG Scholar, Department of MCA, JBIET, Hyderabad (Telangana), India.
3A Anil Kumar, PG Scholar, Department of MCA, JBIET, Hyderabad (Telangana), India.
4M Ravi, Department of MCA, JBIET, Hyderabad (Telangana), India.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3544-3546 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14380982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1438.0982S1119
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Abstract: Programming deformation gauge expect a crucial activity in keeping up extraordinary programming and diminishing the cost of programming improvement. It urges adventure executives to relegate time and advantages for desert slanted modules through early flaw distinguishing proof. Programming flaw desire is a matched portrayal issue which orchestrates modules of programming into both 2 arrangements: Defect– slanted and not-deformation slanted modules. Misclassifying blemish slanted modules as not-disfigurement slanted modules prompts a higher misclassification cost than misclassifying not-flaw slanted modules as deformation slanted ones. The AI estimation used in this paper is a mix of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplace Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The proposed Algorithm is surveyed and demonstrates better execution and low misclassification cost when differentiated and the 3 calculations executed autonomously.
Keywords: Cost-Sensitive Learning; Feature Selection; Software Defect Prediction.
Scope of the Article: Systems and Software Engineering