Software Defect Prediction Using Machine Learning Techniques

Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software-defected data sets are supported and at least partially recognized due to their enormous dimension. This Problem is handled by hybridized approach that includes the PCA, randomforest, naïve bayes and the SVM Software Framework, which as five datasets as PC3, MW1, KC1, PC4, and CM1, are listed in software analysis using the weka simulation tool. A systematic research analysis is conducted in which parameters of confusion, precision, recall, recognition accuracy, etc Are measured as well as compared with the prevailing schemes. The analytical analysis indicates that the proposed approach will provide more useful solutions for device defects prediction.