Mastering Merge Conflicts in Divergent Forks: ICSE 2020 Study

Table of Contents

1. Introduction
2. Understanding Divergent Forks
   1. Definition of Divergent Forks
   2. Examples of Divergent Forks
3. Challenges with Divergent Forks
   1. Maintenance Cost
   2. Conflict Resolution
4. Merge-Conflicts in Divergent Forks
   1. Distribution of Fixes from Merge-Conflicts
   2. Categorization of Build Breaks
5. Proposed Solution: Automatic Patch Procedure
   1. AST-Based Patch Inference
      1. Procedure for Patch Inference
      2. Example of AST-Based Patch Inference
   2. Scalability Issues and Input Commit Pruning
      1. Structure of the Edge Development Process
      2. Pruning Input Commits for Scalability
6. Evaluation and Results
   1. Success Rate of Automatic Patch Procedure
   2. Efficiency of the Pruning Method
7. Conclusion

Towards Understanding and Fixing Upstream Merge Induced Conflicts in Divergent Forks

1. Introduction

In this article, we will discuss the challenges faced in managing divergent forks and propose a solution for effectively handling merge-conflicts. Divergent forks are commonly used for customization or independent development, allowing developers to save time and effort by starting with an existing project. However, maintaining divergent forks can be costly, especially when it comes to merging upstream changes. These merges often result in conflicts, such as build breaks or test failures, which can be time-consuming to resolve.

2. Understanding Divergent Forks

2.1 Definition of Divergent Forks

Before delving into the challenges and solutions, let's first understand what divergent forks are. A divergent fork is created when a project is forked, and changes made in the forked project are not pushed back to the original project. The original project is referred to as the upstream, while the forked project is known as the downstream.

2.2 Examples of Divergent Forks

One of the most popular examples of divergent forks is Chromium-based web browsers. Many web browsers are forked from Chromium, creating divergent forks. This makes studying the nature of divergent forks using data from the Microsoft Edge development process highly Relevant and insightful.

3. Challenges with Divergent Forks

Managing divergent forks comes with its own set of challenges. In this section, we will explore the two main challenges: maintenance cost and conflict resolution.

3.1 Maintenance Cost

While divergent forks are beneficial for project development, they come with a significant maintenance cost. Since these forks are derived from original projects, it is necessary to regularly merge upstream changes for security patches and feature updates. However, frequent merges can Consume a significant amount of engineering work, especially when the upstream code updates frequently.

3.2 Conflict Resolution

Merge-conflicts are inevitable when merging upstream changes into divergent forks. These conflicts can be categorized into textual conflicts, build breaks, and test failures. Among these categories, build breaks pose the most severe challenge. Resolving build breaks requires engineers to analyze the history of upstream changes, which can be time-consuming and complex.

4. Merge-Conflicts in Divergent Forks

To address the challenges of divergent forks, it is crucial to understand the nature and distribution of merge-conflicts. In this section, we will analyze the distribution of fixes from merge-conflicts and categorize the types of build breaks.

4.1 Distribution of Fixes from Merge-Conflicts

Through our study, we have found that build breaks are the most significant issues caused by merge-conflicts in divergent forks. To gain a deeper understanding, we have analyzed the distribution of fixes for merge-conflicts and identified the need for an automated patch procedure.

4.2 Categorization of Build Breaks

Building upon our analysis, we have further categorized the types of build breaks based on their causes and fixes. This categorization allows us to develop a targeted approach for resolving build breaks in divergent forks.

5. Proposed Solution: Automatic Patch Procedure

To mitigate the challenges associated with merge-conflicts in divergent forks, we propose an automatic patch procedure. This procedure consists of two parts: upstream commit pruning and AST-based patch inference.

5.1 AST-Based Patch Inference

The AST-based patch inference approach involves diffing the abstract syntax trees (ASTs) of two versions of upstream files and inferring potential patches. This procedure allows us to identify changes in function definitions and Apply the necessary patches to downstream files.

5.1.1 Procedure for Patch Inference

We will explain our procedure for inferring patches using AST comparisons and demonstrate its effectiveness through an example. By leveraging the consistent changes in ASTs, we can efficiently infer and apply patches to resolve build breaks.

5.1.2 Example of AST-Based Patch Inference

To provide a practical example, we will demonstrate how AST-based patch inference can be used to identify changes in function names and apply the corresponding patches accurately. This example will highlight the efficacy of our proposed approach.

5.2 Scalability Issues and Input Commit Pruning

Although the AST-based approach is effective, it faces scalability challenges due to the size of the source code and the high number of commits. To overcome this issue, we introduce input commit pruning, which focuses on reducing the number of commits considered for the AST-based patch inference.

5.2.1 Structure of the Edge Development Process

To understand the process of input commit pruning, we need to grasp the structure of the Edge development process. By examining the branches and commits involved in the development process, we can identify the potential candidates for input commit pruning.

5.2.2 Pruning Input Commits for Scalability

We will illustrate the process of pruning input commits to improve the scalability of the AST-based patch inference. By strategically selecting the commits responsible for build breaks, we can significantly reduce the number of upstream files considered for patch inference, resulting in a more efficient procedure.

6. Evaluation and Results

In this section, we will evaluate the effectiveness of our proposed automatic patch procedure and discuss the results obtained. We will assess the success rate of the procedure in resolving build breaks and evaluate the efficiency of the input commit pruning method.

6.1 Success Rate of Automatic Patch Procedure

Through our evaluation, we have determined the success rate of our automatic patch procedure in resolving build breaks in divergent forks. This quantitative assessment will provide insights into the effectiveness and reliability of our proposed approach.

6.2 Efficiency of the Pruning Method

We will evaluate the efficiency of the input commit pruning method in reducing the number of upstream files considered for patch inference. This evaluation will highlight the scalability improvements achieved through our pruning technique.

7. Conclusion

In conclusion, managing merge-conflicts in divergent forks can be a challenging task. However, through our research and proposed solution, we can significantly mitigate the maintenance cost and streamline the conflict resolution process. Our automatic patch procedure, combined with input commit pruning, offers an efficient and effective approach to handling merge-conflicts in divergent forks. By leveraging AST-based patch inference and strategic commit selection, we can save valuable engineering time and ensure that divergent forks stay up to date with upstream changes.