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

2025-10-02

AI Summary

Semantic conflict detection in collaborative development suffers from high false-positive rates, primarily because existing lightweight static analyses cannot reliably distinguish behavior-preserving code refactorings from genuine semantic changes. To address this, we propose a refactoring-aware static analysis method that, for the first time, deeply integrates automated refactoring identification into lightweight interference detection—enabling precise filtering of spurious conflicts introduced by refactorings while preserving coverage. We implement an end-to-end detection tool and evaluate it on 99 labeled scenarios and 1,087 real-world merge scenarios. Our approach reduces false positives by 31.8%, significantly improves precision, and incurs only a marginal drop in recall. The method is practical, efficient, and scalable, establishing a new paradigm for semantic conflict detection in collaborative software development.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Detecting semantic interference in collaborative software development
• Reducing false positives caused by behavior-preserving code refactorings
• Improving precision of semantic conflict detection via refactoring awareness

Innovation

• Uses refactoring-aware static analysis for detection
• Incorporates automated refactoring detection to improve precision
• Filters behavior-preserving refactorings to reduce false positives