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

Victor Lira, Paulo Borba, Rodrigo Bonifácio, Galileu Santos e Matheus barbosa

TL;DR

RefFilter addresses noisy semantic interference reports in code merges by adding a refactoring-aware filtering stage that uses RefactoringMiner and ReExtractor+ to identify behavior-preserving changes. It operates as a post-processing layer on top of lightweight static detectors, aligning lines and filtering out interferences that arise solely from refactorings. Evaluations on a 99-scenario ground-truth dataset and a large 1,087-merge dataset show substantial false positive reductions (≈32%) with only minor, statistically insignificant recall loss, demonstrating practical benefits for merge tooling. The work contributes a practical tool, a large-scale dataset for evaluation, and an empirical framework for analyzing refactoring-induced false positives in semantic interference detection.

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.

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

TL;DR

RefFilter addresses noisy semantic interference reports in code merges by adding a refactoring-aware filtering stage that uses RefactoringMiner and ReExtractor+ to identify behavior-preserving changes. It operates as a post-processing layer on top of lightweight static detectors, aligning lines and filtering out interferences that arise solely from refactorings. Evaluations on a 99-scenario ground-truth dataset and a large 1,087-merge dataset show substantial false positive reductions (≈32%) with only minor, statistically insignificant recall loss, demonstrating practical benefits for merge tooling. The work contributes a practical tool, a large-scale dataset for evaluation, and an empirical framework for analyzing refactoring-induced false positives in semantic interference detection.

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.

Paper Structure

This paper contains 19 sections, 3 equations, 5 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Semantic Merge Conflicts
  4. Semantic Conflict Detection Techniques
  5. Refactoring Detection Tools
  6. Motivating Example and Problem Definition
  7. RefFilter
  8. Overview of the Technique
  9. Refactoring-Aware Semantic Conflict Detection Algorithm
  10. Experimental Setup
  11. Datasets
  12. Evaluation Methodology
  13. Experimental Results
  14. Performance Comparison with Existing Methods
  15. Scaling and Diversity Evaluation
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Merge scenario with refactoring (left) and business rule change (right).
  • Figure 2: Overview of RefFilter workflow.
  • Figure 3: Dataset construction workflow.
  • Figure 4: Execution time per scenario.
  • Figure 5: Scaling and diversity results.

Theorems & Definitions (1)

  • definition 1: False Positive of Interference due to Refactoring