A Provenance-Centric Approach to Code Smell Reduction using Human-in-Loop Approach

Code smells are early warning signs of software development whose detection and timely resolution is essential for long-term software quality. Conventional methods used for detecting smells fail to complete the task because they do not consider the evolution of code over time. To overcome this issue this paper suggests a provenance-driven approach to integrate human-in-the-loop approach in code smell detection. The study begins by collecting provenance data from software version control systems. This data includes history of code changes, decisions and developer feedback. Secondly, the automated tools like PMD and FindBugs are used to detect code smells. The results generated by these tools are reviewed and validated by developers to refine the findings. The process works in form of a feedback loop which bring continuous improvement in the method. The system analyzes the provenance data to understand the context of smells and identify the reason behind smell emergence. Finally, we generate refactoring suggestions based on this analysis. To evaluate the performance the proposed method is compared with SonarQube, Checkstyle, PMD and FindBugs using various metrices. Results show that the proposed approach reduces false positives by 30% and increases the detection of complex smells by 25% boosting software maintainability.