Software Defect Density Prediction Using Deep Learning

Abstract

Software defect prediction is the use of various approaches and procedures to discover and anticipate future flaws in a software project before they become costly and disruptive problems. Organizations may use data analysis, machine learning, and historical defect data to make educated decisions about resource allocation, software testing methodologies, and, ultimately, software product quality. Software defect prediction is the technique of identifying software modules that are likely to have flaws. The suggested system is intended for software defect prediction, combining PCA with a software business analysis model and employing Rank SVM as its primary predictive modeling approach. It includes data collecting from historical defects and relevant data sources, preprocessing to clean and convert the data, feature selection to identify critical indicators, and training a PCA using a software business analysis model utilizing Rank SVM. This methodology, once installed, forecasts the risk of faults in software modules based on their characteristics. The system's output influences resource allocation and testing procedures, hence improving software quality and development efficiency. Continuous monitoring and adjustments assure continuing correctness, making this an efficient option for proactive defect control in software development.