Predicting Merge Conflicts in Collaborative Software Development

Abstract

Background. During collaborative software development, developers often use branches to add features or fix bugs. When merging changes from two branches, conflicts may occur if the changes are inconsistent. Developers need to resolve these conflicts before completing the merge, which is an error-prone and time-consuming process. Early detection of merge conflicts, which warns developers about resolving conflicts before they become large and complicated, is among the ways of dealing with this problem. Existing techniques do this by continuously pulling and merging all combinations of branches in the background to notify developers as soon as a conflict occurs, which is a computationally expensive process. One potential way for reducing this cost is to use a machine-learning based conflict predictor that filters out the merge scenarios that are not likely to have conflicts, i.e. safe merge scenarios.Aims. In this paper, we assess if conflict prediction is feasible.Method. We design a classifier for predicting merge conflicts, based on 9 light-weight Git feature sets. To evaluate our predictor, we perform a large-scale study on 267, 657 merge scenarios from 744 GitHub repositories in seven programming languages.Results. Our results show that we achieve high f1-scores, varying from 0.95 to 0.97 for different programming languages, when predicting safe merge scenarios. The f1-score is between 0.57 and 0.68 for the conflicting merge scenarios.Conclusions. Predicting merge conflicts is feasible in practice, especially in the context of predicting safe merge scenarios as a pre-filtering step for speculative merging.

Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis

“…Merge conflicts are frequent and annoying, and studies have shown they can have a significant impact on the development lifecycle of projects [1]. Prior works have focused on predicting merge conflicts [2]- [7], resolving them through using structured abstract syntax tree (AST) aware techniques [8]- [12], or even verifying semantic correctness of merges using program verifiers [13]. Although these approaches have paved the course for research on merge conflict prediction and resolution, the problem of mergeconflicts persists in practice.…”

Merging problems in modern version control systems

“…In this process, a merge is performed between local modifications and remote modifications. If developers make a lot of modifications in the same part of code, when they incorporate these modifications, Git cannot decide which modify to choose, in this case, the developers need to resolve the conflict manually, which is an error-prone and time-consuming task [4]. Conflicts might be detected in different stages, during merging or testing, since detecting and resolving conflicts often is a difficult task [7].…”

“…Semi-structured merge tools, for example, JDime tool, enhance performance by use ASTs level. A structured merge is then used for the main nodes of the tree, while the unstructured merge is used for the method bodies in the leaves [4]. Semi-structured merge tools have many features, where the merge reports fewer conflicts [2].…”

“…To detect conflicts early, there are many tools helps to increase the developer's awareness. Awareness about the modifications team members may be making is a key factor in team productivity and reduces the number of conflicts [4]. These tools monitor all the modifications developers make in their local repositories and after detecting conflicts among those modifications, it will send notifications to the involved developers [5], examples of some tools, Elvin and Syde are tools supporting direct conflict by increasing awareness through sharing the code change present in other developers' workspaces.…”

“…Distributed Version Control Systems (DVCSs), such as Git tool and hosting platforms, such as GitHub, facilitates the software development process. Despite these advanced tools, still there merging and integration problems [4]. To better detect and resolve code integration conflicts, researchers have proposed methods that use different strategies to decrease effort and improve the correctness of the integration [2].…”