A Technique for Improving Software Quality using Support Vector Machine - Devi, J; Sehgal, N (International Journal of Computer Sciences and Engineering, 2025)
Today software has reformed the key element on every environment. Quality of software is connected with the number of faults as well as it determinate by time and cost. Software is a process and maintains continuous change to improve the functionality and effectiveness of the software quality. During the life cycle of software various problems arises like advanced planning, well documentation and proper process control. Software defects are expensive in specification of cost and quality. Software defect prediction improves quality framework predictive techniques and software metrics to provide fault-prone module description. This paper main feature is the concept of change proneness and software prediction model used to control the classes of software which are often to change. We have two aspects to be inscribed Parameters like Accuracy, Precision, Recall and Receiver operating characteristics (ROC). Machine learning algorithms are used for predicting software. This paper is proposing to relate and compare all machine learning techniques interrelated to performance parameters.