Machine learning Methods for Software Defect Prediction a Revisit
Y. VenkataRaghava Rao1, Rama Devi Burri2; V.B.V.N.Prasad3
1Y. Venkata RaghavaRao, Professor, Department of Computer Science and Engineering, in CSE, India.
2Rama Devi Burri, Associate professor, Department of I.T., Lakireddy Balireddy college of Engineering, Mylavaram, Krishna (Dt), (A.P), India.
3V.B.V.N. Prasad, Professor, Department of Mathematics, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur (Dt), (A.P), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 3431-3435 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7287068819/19©BEIESP
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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: Software defect prediction (SDP) is a challenging factor in the area of Computer Science. Software engineering is the fertile ground to each and every computer science project, which results the computers the feature to develop the planning on an accurate job by means data. ML Machine based learning was enhanced by those implemented research on Pattern Identification with Computational intelligence based on Artificial Intelligence (AI)”. These (ML) Machine based knowledge tactics are boosted in resolving those faults which are occurring from validation in addition with Domain based systems. Those programming-based difficulties which are designated as the procedure-oriented knowledge with in those situations and alterations. A predictive model which is measured into two ways. First one is Defective Module and second one is Non-defective Module. The two predictive modules are formed by using (ML) Machine Learning techniques. Machine learning methods be cooperative in software defect prediction. For the existing data sets are collected from NASA and Eclipse from promise repository which is a motivated version of UCI repository which is developed in 2005. We have a lot of learning ways to notice defects in software. Here we are revisiting the ML methods for SDP (software defect prediction).
Keyword: ML Techniques, Software defect prediction, Predictive analytics, Performance measures.
Scope of the Article: Machine Learning.