Performance of Various Computational Intelligence Methods in Software Defect Prediction: An Analytical Perspective

Abstract

Success of any IT industry is depends on how much Quality of Software (QoS)developed; its turn by engineering methodology enables production of QoS, Software defects can influence the quality of software. Probability of defect occurrences can directly affect to QoS. An Software Defect Prediction (SDP) is efficient whenever intelligence algorithm accuracy and fault detection rate is high. In this paper provides a systematic review on SDP by using various computational intelligence methods and most popular methods for fault prediction. When the size of software is huge it becomes challenge to predict the software defect,Six algorithms were analysed using Weka tool apply various intelligence algorithms on JM1 dataset and calculating accuracy and fault prediction rate, and Finally we should understandone classifiers is good in terms of good accuracy of SDP over the other algorithms.