Extracting Fix Patterns for Static Analysis Violations Based on Collective Developer Knowledge

ABSTRACT Introduction Much of the effort spent on software development is allocated on detecting and fixing bugs or, more generally, violations that lead to erroneous or inefficient code. Although static analysis tools aspire to automate bug detection, their usage is typically limited to code style rules and typical violations, while they only provide generic instructions for bug fixing. As a result, contemporary approaches focus on extracting bug‐fix patterns from source code revisions (commits). Methodology In this work, we build a methodology that extracts fix patterns for violations detected by the PMD static analysis tool. In contrast to current approaches, which employ specific data sources and focus on particular violations, our system extracts source code edits from multiple GitHub repositories and maps them into 36 different types of violations. By employing a detailed syntax tree representation and a tree edit distance technique, we build a similarity scheme for source code edits, which is used to group them into clusters. We employ two clustering algorithms, K‐medoids and DBSCAN, and further optimize them using a purity metric to produce clusters that correspond to specific fixes. Results Our evaluation indicates that DBSCAN extracts more cohesive clusters (purity more than 0.9), which effectively target specific PMD rules, while K‐medoids extracts generic clusters (purity around 0.7) that pinpoint common edits. Conclusion Finally, upon analyzing the diversity of the sources from which fixes are derived (commits and repositories), we conclude that they are generic enough to reflect how similar issues are dealt with by the developer community.