Software Defect Prediction Based on Support Vector Classifier and Rule Mining - Fareha Bashir; Dr. Akbar Shaun (International Journal of Engineering and Management Research, 2023)
Software defect prediction plays a crucial role in ensuring the quality and reliability of software systems. Rule mining-based approaches have gained popularity in this domain as they provide insights into the relationships between software metrics and the occurrence of defects. This abstract presents an overview of software program defect prediction based totally on rule mining. The process begins with the collection of historical data from previous software projects, encompassing defect records and associated software metrics. Relevant features are extracted from the data, including static code analysis metrics, change metrics, process metrics, and dynamic metrics. The collected data is then prepared by addressing data quality issues, handling missing values, and splitting it into training and testing sets. Using a rule mining algorithm, such as association rule mining or decision tree induction, patterns and rules are discovered that correlate the software metrics with defect occurrences. The goal is to identify rules with high support and confidence, indicating strong associations between specific metrics and defect-prone areas of the software. The discovered rules are evaluated using appropriate metrics, such as precision, recall, F1 score, or AUC-ROC, to assess their effectiveness in predicting defects. Once validated, the rules are applied to new software projects, where the software metrics are fed into the rule model to classify components as defect-prone or defect-free. Continuous validation and improvement of the defect prediction model are necessary to ensure its accuracy and performance. This involves incorporating new data, refining the rules or metrics, and adapting the model to changing software development practices. Software defect prediction based totally on rule mining offers a valuable approach for identifying potential defects early in the software development lifecycle. By leveraging historical data and discovering meaningful relationships between software metrics and defects, organizations can proactively allocate resources and implement preventive measures to improve software quality and reliability.