RefFilter: Improving Semantic Conflict Detection via Refactoring-Aware Static Analysis

Abstract

Detecting semantic interference remains a challenge in collaborative software development. Recent lightweight static analysis techniques improve efficiency over SDG-based methods, but they still suffer from a high rate of false positives. A key cause of these false positives is the presence of behavior-preserving code refactorings, which current techniques cannot effectively distinguish from changes that impact behavior and can interfere with others. To handle this problem we present RefFilter, a refactoring-aware tool for semantic interference detection. It builds on existing static techniques by incorporating automated refactoring detection to improve precision. RefFilter discards behavior-preserving refactorings from reports, reducing false positives while preserving detection coverage. To evaluate effectiveness and scalability, use two datasets: a labeled dataset with 99 scenarios and ground truth, and a novel dataset of 1,087 diverse merge scenarios that we have built. Experimental results show that RefFilter reduces false positives by nearly 32% on the labeled dataset. While this reduction comes with a non significant increase in false negatives, the overall gain in precision significantly outweighs the minor trade-off in recall. These findings demonstrate that refactoring-aware interference detection is a practical and effective strategy for improving merge support in modern development workflows.