Merging is common in collaborative software development, often leading to conflicts. Code modifications, such as refactorings, may contribute to merge conflicts depending on the approach employed by the merging tools. In this paper, we investigate how code changes over time influence merge conflicts and examine how different merge tools affect their frequency. We analyzed over 507,000 merge actions from GitHub Java projects using three distinct merge tools: Git, jFSTMerge, and IntelliMerge. Our findings reveal that nearly 43% of Git’s conflicting scenarios involved refactorings, and resolving these conflicts required significantly more time. We found that refactorings increase the likelihood of conflicts by roughly 10 times in Git, 12 times in jFSTMerge, and 9 times in IntelliMerge. Additionally, out of 62 refactoring types executed, 33.8% were consistently associated with conflicts across all three tools. These insights may be employed to enhance merging algorithms to better handle these specific types of changes and to guide development teams in mitigating risks by coordinating refactorings, potentially reducing the overall rate of conflicts.
Fri 7 MarDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Change Management & Program ComprehensionReproducibility Studies and Negative Results (RENE) Track / Research Papers / Early Research Achievement (ERA) Track at L-1710 Chair(s): Masud Rahman Dalhousie University | ||
11:00 15mTalk | AdvFusion: Adapter-based Knowledge Transfer for Code Summarization on Code Language Models Research Papers Iman Saberi University of British Columbia Okanagan, Amirreza Esmaeili University of British Columbia, Fatemeh Hendijani Fard University of British Columbia, Chen Fuxiang University of Leicester | ||
11:15 15mTalk | EarlyPR: Early Prediction of Potential Pull-Requests from Forks Research Papers | ||
11:30 15mTalk | The Hidden Challenges of Merging: A Tool-Based Exploration Research Papers Luciana Gomes UFCG, Melina Mongiovi Federal University of Campina Grande, Brazil, Sabrina Souto UEPB, Everton L. G. Alves Federal University of Campina Grande | ||
11:45 7mTalk | On the Performance of Large Language Models for Code Change Intent Classification Early Research Achievement (ERA) Track Issam Oukay Department of Software and IT Engineering, ETS Montreal, University of Quebec, Montreal, Canada, Moataz Chouchen Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada, Ali Ouni ETS Montreal, University of Quebec, Fatemeh Hendijani Fard University of British Columbia | ||
11:52 15mTalk | Revisiting Method-Level Change Prediction: Comparative Evaluation at Different Granularities Reproducibility Studies and Negative Results (RENE) Track Hiroto Sugimori School of Computing, Institute of Science Tokyo, Shinpei Hayashi Institute of Science Tokyo DOI Pre-print | ||