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Analysis of Variance Carried Out For Software Defect Prediction

Swathi. K, arun Birader

Abstract


Software Defect Prediction (SDP) is an essential activity in testing phase of Software Development Life Cycle. It recognizes the modules that are defect prone and needs broad testing. In this way, the testing assets can be used proficiently without violating the limitation. However, Software Defect Prediction (SDP) is very supportive in testing; it’s not always easy to envisage the defective modules. There are several matters that obstruct the smooth performance as well as use of the blemish prediction models. In present research, a survey on different software firms to discover and analyze their software prediction models, carry out a SWOT (Strength Weakness Opportunities Threats) analysis of each model, identify non value added activities in these models using VSM (Value Stream Mapping) and FMEA (Failure Model Effective Analysis) as a tool and lastly come out with an adoptive model which will have benefits of all models and can be extensively used.


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References


Pooja Paramshetti D. A. Phalke Survey on Software Defect Prediction Using Machine Learning Techniques International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Impact Factor (2012): 3.358 Volume 3 Issue 12, December 2014.

Xiaoxing Yang, Ke Tang, Senior Member, IEEE, and Xin Yao, Fellow, IEEE. A Learning-to-Rank Approach to Software Defect Prediction, This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination (2015).

Baljinder Ghotra, Shane McIntosh, Ahmed E. Hassan Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , Software Analysis and Intelligence Lab (SAIL) School of Computing, Queen’s University, Canada (2014).


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