A predictive approach to estimate software defects density using Probabilistic Neural Networks for the given Software Metrics
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 7
Abstract
Software plays a very important role in our everyday life.There are many instances which show that even a small defect in the software can cause huge loss to many lives.By evaluating the errors in software in various phases,we can reduce the lateral cost of software.Software quality prediction has been an important arena since the las two decades. Several models and techniques have been proposed and utilized in this gaze.We can recognize the areas which are prone to hazards with the help of logic of quality prediction. In the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using ten software metrics of these four phases.at the end of each phase the defect density indicator will be taken as an input for the next phase.With the help of ANN and PNN strategies,we have extnded our work. The experimental results are compared with fuzzy, ANN and PNN.Incomparision with ANN and fuzzy,the number of defects can be discovered better with ANN strategy and with PNN strategy there is better prediction in the reliability of mertics of SDLC.Compared to the two implementation methods used on all datasets,ProbabilisticNueral Networks have better reliability.Rather than depending on a singlrtechnique,it would be better to use a scope of software defect prediction models.Experimental results will be performed by utilizing matlab tool.
Authors and Affiliations
T. Ravi Kumar, Dr. T. Srinivasa Rao, Dr. Ch. V. M. K. Hari
Keywords
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- EP ID EP394952
- DOI 10.9790/9622-0807020815.
- Views 124
- Downloads 0
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