Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns
Existing approaches for detecting repetitive code changes relying on syntactic similarity cannot effectively detect semantic change patterns. In this work, we introduce a novel graph-based mining approach, CPatMiner, which is capable of detecting semantic code change patterns from a large number of open-source repositories by capturing dependencies between fine-grained change elements.
We evaluated CPatMiner by mining change patterns in a diverse corpus of 5,000+ open-source projects from GitHub with 170,000+ developers. We use three complementary methods. First, we sent the mined patterns to the authors and received 108 responses. 70% of respondents recognized those patterns as their meaningful frequent changes. 79% of respondents even named the patterns, and 44% wanted IDEs to automate such repetitive changes. The mined patterns belong to various activities: adaptive (9%), perfective (20%), corrective (35%) and preventive (36%). Second, we compared CPatMiner with the state-of-the-art, AST-based technique, and reported that CPatMiner detects 2.1x more meaningful patterns. Third, we used CPatMiner to search for patterns in a corpus of 88 GitHub projects with longer histories consisting of 164M SLOCs. It constructed 322K fine-grained change graphs containing 3M nodes, and detected 17K change patterns which provide unique insights on the practice of change patterns among individuals and teams. We found that a large percentage (75%) of the patterns from individual developers are commonly shared with others, and this holds true for teams. Moreover, we found that the patterns spread widely over time. Thus, we call for a community-based change pattern database to provide important resources in novel applications.
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|Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change PatternsTechnical Track
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