Merging
Student Projects
- CI/CD-Enhanced Conflict Resolution: Feasibility and Potential for Smarter Merging
| Student Projects
Context
Concurrent editing in cooperative software development often results in merge conflicts. Human effort is needed to resolve such merge conflicts. Studying merge conflicts and how to (semi-)automatically resolve them is an active research area.
- Evaluating Refactoring-Aware Merging: An Experimental Study
| Student Projects
Context
Traditional merge tools (e.g., Git merge) often treat refactorings as conflicting changes, leading to unnecessary conflicts. On the contrary, a refactoring-aware approach may recognize and account for refactorings, preventing false positives in conflict detection.
- Investigating best Deep Learning architectures for merge conflict resolution data
| Student Projects
Context
Merge conflict resolution is a critical challenge in software development, particularly in large, collaborative projects that use version control systems like Git. When multiple developers modify the same part of a codebase, conflicts arise that require manual intervention. Existing automated resolution strategies often rely on rule-based approaches or traditional machine learning models, which struggle with complex and ambiguous cases. Deep Learning has the potential to improve conflict resolution by learning patterns from historical merge conflicts and predicting optimal resolution strategies. However, identifying the most effective Deep Learning architecture for this task remains an open question.