Merge Conflict Resolution
Deep Learning for Software Merge Conflict Resolution | Current Topics
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.
Feature Engineering for Classification-Based Merge Conflict Resolution | Current Topics
Context
Merge conflict resolution remains a significant challenge in Git-based software development, as manual conflict resolutions slow down collaboration and reduce developer productivity. However, empirical research results suggest that a vast majority of chunk resolutions found in practice can be derived from a fixed set of conflict resolution patterns, combining the ours, theirs, and base parts of a conflicting chunk in a pre-defined way. These findings form the foundation for phrasing merge conflict resolution as a classification problem, and thus using traditional machine learning for predicting conflict resolutions.
Predicting Merge Conflict Resolutions: WSRC vs. Random Forest | Current Topics
Context
Merge conflict resolution remains a significant challenge in Git-based software development, as manual conflict resolutions slow down collaboration and reduce developer productivity. However, empirical research results suggest that a vast majority of chunk resolutions found in practice can be derived from a fixed set of conflict resolution patterns, combining the ours, theirs, and base parts of a conflicting chunk in a pre-defined way. These findings form the foundation for phrasing merge conflict resolution as a classification problem, and thus using traditional machine learning for predicting the correct resolution.