Intelligent Version Control Conflict Resolution Using ML

  • Authors

    • Robert Miller Software Engineering Manager, TechNova Inc, USA. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2020PI7N2R

    Published 06-02-2020

  • Version Control, Merge Conflicts, Machine Learning, Conflict Resolution, Collaborative Software Development, Software Engineering Automation

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    R. Miller, “Intelligent Version Control Conflict Resolution Using ML”, IJMLPA, vol. 3, no. 1, pp. 01–10, Jun. 2020, doi: 10.67228/3142788X/IJMLPA-2020PI7N2R.

  • Abstract

    Version control systems are essential for collaborative software development, but merge conflicts remain a persistent challenge, often leading to delays, errors, and increased maintenance effort. Traditional conflict resolution methods rely heavily on manual intervention, which is time-consuming and error-prone, especially in large, distributed teams. This paper proposes an intelligent, machine learning-based approach to automatically detect, classify, and resolve version control conflicts. By leveraging historical commit data, code change patterns, and contextual information from source code and documentation, the proposed system predicts optimal resolutions and provides recommendations to developers. Experiments on open-source and industrial repositories demonstrate the effectiveness of ML models in reducing conflict resolution time, improving accuracy, and supporting team productivity. The study highlights the potential of AI-driven tools to transform collaborative software development and minimize merge-related risks.

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