MigMate: A VS Code Extension for LLM-based Library Migration of Python Projects

• URL: http://arxiv.org/abs/2603.01596v1
• Date: Mon, 02 Mar 2026 08:26:31 GMT
• Title: MigMate: A VS Code Extension for LLM-based Library Migration of Python Projects
• Authors: Matthias Kebede, May Mahmoud, Mohayeminul Islam, Sarah Nadi,
• Abstract summary: Our previous research developed MigrateLib, a command-line LLM-based migration tool.<n>MigMate builds on MigrateLib by integrating the automated migration process into the developer's existing development environment.<n>A preliminary user study shows that plugin usage consistently reduces the time taken to complete a library migration task.
• Score: 0.8586348698580818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern software relies heavily on third-party software libraries to streamline the development process. The act of switching one library for a similar counterpart, called library migration, naturally occurs as libraries become outdated or unsuitable for the project. Manually migrating from one library to another is a time-consuming task. Our previous research developed MigrateLib, a command-line LLM-based migration tool that can automate the complete migration process. In this paper, we present our open-source VS Code IDE plugin, MigMate, that builds on MigrateLib by integrating the automated migration process into the developer's existing development environment. MigMate provides an interactive experience, allowing developers to view and confirm changes before they are applied. A preliminary user study shows that plugin usage consistently reduces the time taken to complete a library migration task, and it scores highly on the System Usability Scale.

Related papers

• SPELL: Synthesis of Programmatic Edits using LLMs [10.41623927140964]
  Library migration is a common but error-prone task in software development.<n>We present a new approach to automated API migration that sidesteps the limitations described above.
  arXiv  Detail & Related papers  (2026-02-01T09:03:56Z)
• PyMigTool: a tool for end-to-end Python library migration [0.8586348698580818]
  We develop an end-to-end solution that can automatically migrate code between any arbitrary pair of Python libraries.<n>We first study the capabilities of Large Language Models (LLMs) for library migration on a benchmark of 321 real-world library migrations.<n>We find that LLMs can effectively perform library migration, but some post-processing steps can further improve the performance.
  arXiv  Detail & Related papers  (2025-10-09T20:54:26Z)
• Analyzing C/C++ Library Migrations at the Package-level: Prevalence, Domains, Targets and Rationals across Seven Package Management Tools [11.76396912076385]
  This paper analyzes 19,943 C/C++ projects that utilize different package management tools and establishes the first C/C++ library migration dataset.<n>We find that the overall trend in the number of C/C++ library migrations is similar to Java.<n>We find four C/C++-specific migration reasons, such as less compile time and unification of dependency management.
  arXiv  Detail & Related papers  (2025-07-04T02:44:38Z)
• An Empirical Study of Python Library Migration Using Large Language Models [0.8003337912268735]
  Large Language Models (LLMs) are shown to be effective at generating and transforming code as well as finding similar code.<n>We evaluate three LLMs, LLama 3.1, GPT-4o mini, and GPT-4o on PyMigBench, where we migrate 321 real-world library migrations.<n>LLama 3.1, GPT-4o mini, and GPT-4o migrations pass the same tests that passed in the developer's migration.
  arXiv  Detail & Related papers  (2025-04-17T18:32:48Z)
• MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions [53.811953357289866]
  Large language models (LLMs) have shown remarkable progress across various domains.<n>LLMs struggle with incomplete code context understanding and inaccurate migration point identification.<n>MigGPT is a framework that employs a novel code fingerprint structure to retain code snippet information.
  arXiv  Detail & Related papers  (2025-04-13T08:08:37Z)
• Towards Modular LLMs by Building and Reusing a Library of LoRAs [64.43376695346538]
  We study how to best build a library of adapters given multi-task data. We introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters. To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters.
  arXiv  Detail & Related papers  (2024-05-18T03:02:23Z)
• Executable Code Actions Elicit Better LLM Agents [76.95566120678787]
  This work proposes to use Python code to consolidate Large Language Model (LLM) agents' actions into a unified action space (CodeAct) integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language.
  arXiv  Detail & Related papers  (2024-02-01T21:38:58Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• Code Librarian: A Software Package Recommendation System [65.05559087332347]
  We present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code.
  arXiv  Detail & Related papers  (2022-10-11T12:30:05Z)
• Characterizing Python Library Migrations [2.2557806157585834]
  We label 3,096 migration-related code changes in 335 Python library migrations. We find that 40% of library pairs have API mappings that involve non-function program elements. On average, a developer needs to learn about 4 APIs and 2 API mappings to perform a migration.
  arXiv  Detail & Related papers  (2022-07-03T21:00:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.