This program is tentative and subject to change.
Merge conflicts in distributed version control systems (DVCS) like Git are a persistent challenge in software development lifecycle. If not handled properly or overlooked, they can lead to issues like hindering collaboration and introducing errors. While automated resolution methods exist, prevailing approaches—such as multi‑class classification and direct code generation—often suffer from limited interpretability, demanding substantial manual effort to refine predictions, and risk producing subtly flawed code. Critically, existing research often overlooks a prevalent conflict type: adjacent-line conflicts, where independent edits to contiguous lines are flagged by tools like Git. Our empirical analysis reveals that these make up a substantial portion of all conflicts. Moreover, they can often be resolved using simple patterns.
Motivated by these limitations and empirical findings, we propose a novel approach: modeling merge conflict resolution as edit script selection. Instead of predicting abstract categories or generating code de novo, our method makes a binary decision for each atomic line-level edit script contributing to the conflict: accept or reject. Our method inherently makes the reasoning behind proposed solutions transparent, as decisions directly correspond to individual, developer-authored code modifications. It also aligns closely with how developers naturally approach conflict analysis by considering each change in context. Our method applies for the vast majority (94.18%) of conflicts that do not require entirely new code; this selection process directly yields the resolved code by applying the chosen subset of existing edits.
As an implementation of our proposed method, we developed EditFusion, a deep learning model that performs edit script selection by leveraging semantic embeddings and edit metadata. Extensive evaluation on large-scale, real-world datasets demonstrates both the prevalence of adjacent-line conflicts and EditFusion’s superior performance in accurately resolving conflicts compared to baselines. Our work represents an attempt towards more transparent, intuitive, and practical automated merge conflict resolution.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
14:00 - 15:30 | |||
14:00 10mTalk | Exploring Static Taint Analysis in LLMs: A Dynamic Benchmarking Framework for Measurement and Enhancement Research Papers Haoran Zhao Fudan University, Lei Zhang Fudan University, Keke Lian Fudan University, Fute Sun Fudan University, Bofei Chen Fudan University, Yongheng Liu Fudan University, Zhiyu Wu Fudan University, Yuan Zhang Fudan University, Min Yang Fudan University | ||
14:10 10mTalk | EPSO: A Caching-Based Efficient Superoptimizer for BPF Bytecode Research Papers Qian Zhu Nanjing University, Yuxuan Liu Nanjing University, Ziyuan Zhu Nanjing University, Shangqing Liu Nanjing University, Lei Bu Nanjing University | ||
14:20 10mTalk | GNNContext: GNN-based Code Context Prediction for Programming Tasks Journal-First Track Xiaoye Zheng Zhejiang University, Zhiyuan Wan Zhejiang University, Shun Liu Zhejiang University, Kaiwen Yang Zhejiang University, David Lo Singapore Management University, Xiaohu Yang Zhejiang University | ||
14:30 10mTalk | R3-Bench: Reproducible Real-world Reverse Engineering Dataset for Symbol Recovery Research Papers Muzhi Yu Peking University and Alibaba Group, Zhengran Zeng Peking University, Wei Ye Peking University, Jinan Sun Peking University, Xiaolong Bai Alibaba Group, Shikun Zhang Peking University | ||
14:40 10mTalk | Protecting Source Code Privacy When Hunting Memory Bugs Research Papers Jielun Wu Nanjing University, Bing Shui Nanjing University, Hongcheng Fan Nanjing University, Shengxin Wu Nanjing University, Rongxin Wu Xiamen University, Yang Feng Nanjing University, Baowen Xu Nanjing University, Qingkai Shi Nanjing University | ||
14:50 10mTalk | Latra: A Template-Based Language-Agnostic Transformation Framework for Effective Program Reduction Research Papers Zhenyang Xu University of Waterloo, Yiran Wang University of Waterloo, Yongqiang Tian Monash University, Mengxiao Zhang University of Waterloo, Chengnian Sun University of Waterloo | ||
15:00 10mTalk | When Control Flows Deviate: Directed Grey-box Fuzzing with Probabilistic Reachability Analysis Research Papers Peihong Lin National University of Defense Technology, Pengfei Wang National University of Defense Technology, Xu Zhou National University of Defense Technology, Wei Xie University of Science and Technology of China, Xin Ren National University of Defense Technology, Kai Lu National University of Defense Technology, China | ||
15:10 10mTalk | EditFusion: Resolving Code Merge Conflicts via Edit Selection Research Papers Changxin Wang Nanjing University, Yiming Ma Nanjing University, Lei Xu Nanjing University, Weifeng Zhang Nanjing University of Posts and Telecommunications | ||
15:20 10mTalk | Detecting Semantic Clones of Unseen Functionality Research Papers Konstantinos Kitsios University of Zurich, Francesco Sovrano Collegium Helveticum, ETH Zurich, Switzerland; Department of Informatics, University of Zurich, Switzerland, Earl T. Barr University College London, Alberto Bacchelli University of Zurich Pre-print | ||