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

• URL: http://arxiv.org/abs/2510.01960v1
• Date: Thu, 02 Oct 2025 12:30:49 GMT
• Title: RefFilter: Improving Semantic Conflict Detection via Refactoring-Aware Static Analysis
• Authors: Victor Lira, Paulo Borba, Rodrigo Bonifácio, Galileu Santos e Matheus barbosa,
• Abstract summary: RefFilter is a SDG-aware tool for semantic interference detection.<n>It builds on existing static techniques by incorporating automated detection to improve precision.<n>Results show that RefFilter reduces false positives by nearly 32% on the labeled dataset.
• Score: 2.4000626364733684
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Semantics-Preserving Evasion of LLM Vulnerability Detectors [14.476903104601154]
  LLM-based vulnerability detectors are increasingly deployed in security-critical code review.<n>We evaluate detection-time integrity under a semantics-preserving threat model.<n>We introduce a metric of joint robustness across different attack methods/carriers.
  arXiv  Detail & Related papers  (2026-01-30T20:54:27Z)
• The Semantic Trap: Do Fine-tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern? [14.472036099680961]
  We propose TrapEval, a comprehensive evaluation framework designed to disentangle vulnerability root cause from functional pattern.<n>We fine-tune five representative state-of-the-art LLMs across three model families and evaluate them under cross-dataset testing, semantic-preservings, and varying degrees of semantic gap measured by CodeBLEU.<n>Our findings serve as a wake-up call: high benchmark scores on traditional datasets may be illusory, masking the model's inability to understand the true causal logic of vulnerabilities.
  arXiv  Detail & Related papers  (2026-01-30T07:19:17Z)
• Probing Pre-trained Language Models on Code Changes: Insights from ReDef, a High-Confidence Just-in-Time Defect Prediction Dataset [0.0]
  We present ReDef, a high-confidence benchmark of function-level modifications curated from 22 large-scale C/C++ projects.<n>Defective cases are anchored by revert commits, while clean cases are validated through post-hoc history checks.<n>This pipeline yields 3,164 defective and 10,268 clean modifications, offering substantially more reliable labels than prior existing resources.
  arXiv  Detail & Related papers  (2025-09-11T07:07:11Z)
• Refactoring $\neq$ Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis [54.361900378970134]
  Just-in-time defect prediction (JIT-DP) aims to predict the likelihood of code changes resulting in software defects at an early stage.<n>Prior research has largely ignored code during both the evaluation and methodology phases, despite its prevalence.<n>We propose Code chAnge Tactics (CAT) analysis to categorize code and its propagation, which improves labeling accuracy in the JIT-Defects4J dataset by 13.7%.
  arXiv  Detail & Related papers  (2025-07-25T23:29:25Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learning [4.014524824655106]
  Static feature-based Android malware detection using machine learning (ML) remains critical due to its scalability and efficiency.<n>Existing approaches often overlook security-critical concerns.<n>We propose a more rigorous methodology for model selection and evaluation.
  arXiv  Detail & Related papers  (2024-09-11T16:37:50Z)
• Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments [20.307151769610087]
  Continual Test-Time Adaptation (CTTA) has emerged as a promising technique to gradually adapt a source-trained model to continually changing target domains.<n>We present AMROD, featuring three core components, to tackle these challenges for detection models in CTTA scenarios.<n>We demonstrate the effectiveness of AMROD on four CTTA object detection tasks, where AMROD outperforms existing methods.
  arXiv  Detail & Related papers  (2024-06-24T08:30:03Z)
• FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools [18.927121513404924]
  Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. Previous research efforts have explored learning-based methods to validate the reported warnings. We propose FineWAVE, a learning-based approach that verifies bug-sensitive warnings at a fine-grained granularity.
  arXiv  Detail & Related papers  (2024-03-24T06:21:35Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection [12.529028629599349]
  We propose a novel benchmarking methodology to help researchers better evaluate the true capabilities and limits of ML4VD techniques. Using six ML4VD techniques and two datasets, we find (a) that state-of-the-art models severely overfit to unrelated features for predicting the vulnerabilities in the testing data, and (b) that the performance gained by data augmentation does not generalize beyond the specific augmentations applied during training.
  arXiv  Detail & Related papers  (2023-06-28T08:41:39Z)
• An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph [3.3598755777055374]
  Current vulnerability screening techniques are ineffective at identifying novel vulnerabilities or providing developers with code vulnerability and classification. To address these issues, we propose a joint multitasked unbiased vulnerability classifier comprising a transformer "RoBERTa" and graph convolution neural network (GCN) We present a training process utilizing a semantic vulnerability graph (SVG) representation from source code, created by integrating edges from a sequential flow, control flow, and data flow, as well as a novel flow dubbed Poacher Flow (PF)
  arXiv  Detail & Related papers  (2023-04-17T20:54:14Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.