A Review Paper on Software Defect Prediction Based on Rule Mining

Abstract

Software defect prediction is an important task in software engineering, aimed at identifying and mitigating software defects before they become major problems. Rule mining is a technique used to discover interesting patterns and relationships in data, and can be applied to software defect prediction by analyzing past data on software development and testing. This abstract discusses the process of software defect prediction based on rule mining, including data collection, data pre-processing, feature extraction, rule mining, model evaluation, and model deployment. By accurately predicting the likelihood of defects occurring in future software releases, developers can take proactive measures to prevent defects from occurring, thereby improving software quality and reducing the time and resources spent on fixing bugs.