Empirical Study on Merge Conflict Dynamics: The Role of Personas in Merge Resolutions

Context

Merge conflicts are a common challenge in collaborative software development, requiring developers to manually resolve inconsistencies between different code versions. Prior research has explored automated approaches to merge conflict resolution, but the impact of developer behavior and personas on the merge process remains not fully investigated [1].

Motivation

This project seeks to investigate the role of developer personas in merge conflict resolution. We aim to understand whether certain developers exhibit conservative or defensive merging behaviors, how these behaviors affect merge outcomes, and whether certain developer traits correlate with suboptimal merges or increased bug introduction. The findings could inform better conflict resolution strategies and improve merge automation techniques.

Goal

The goal of this project is to conduct an empirical study on merge conflict dynamics, focusing on the influence of developer personas on merge resolutions. Specifically, the study will address the following research questions:

• Q1 (Merge personas): Do developers exhibit identifiable merging patterns? Are there developers who are particularly conservative about their code quality or, conversely, more inclined to merge frequently? What behavioral patterns can we observe?
• (For thesis) Q2 (Merge confounding factors): Is there a correlation between complex merge scenarios, poor merge performance, and developer personas?
• (For thesis) Q3 (Sub-optimal merges): Do certain merges lead to suboptimal resolutions due to developer personality traits? Can these cases be identified, and should they be filtered out in future studies? Have such merges introduced bugs?

Tasks & Steps

1. Data Collection:
   • Gather data from a new project repository.
   • Collect relevant datasets from repositories used in recent studies.
2. Empirical Study:
   • Analyze developer merge behavior and categorize personas.
   • Investigate correlations between merge complexity, developer traits, and merge success rates.
   • Identify suboptimal merges and assess their impact on software quality.

Requirements

• Proficiency in Python for data analysis.
• Knowledge of Git and version control systems.
• Familiarity with empirical software engineering and data-driven research methods is beneficial.

Reference

• [1] Alexander Boll, Yael Van Dok, Manuel Ohrndorf, Alexander Schultheiß, Timo Kehrer:“Towards Semi-Automated Merge Conflict Resolution: Is It Easier Than We Expected?” EASE 2024: 282-292.