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

Source: arXiv - 2603.01596v1

Overview

The paper introduces MigMate, an open‑source VS Code extension that brings LLM‑driven library migration directly into developers’ everyday IDE. Building on the authors’ earlier command‑line tool MigrateLib, MigMate lets you swap out outdated or unsuitable Python packages with modern alternatives while staying inside the editor, previewing changes, and confirming them interactively. Early user testing shows noticeable speed‑ups and strong usability scores.

Key Contributions

• IDE‑integrated migration workflow: Seamlessly embeds the full migration pipeline (analysis, code transformation, verification) into VS Code.
• Interactive change review: Developers can inspect, accept, or reject each suggested edit before it’s applied, reducing the risk of accidental breakage.
• Open‑source plugin: The extension is publicly available, encouraging community extensions and real‑world adoption.
• Empirical validation: A preliminary user study demonstrates reduced migration time and high System Usability Scale (SUS) ratings compared with the command‑line baseline.

Methodology

1. Library‑migration model: The authors reuse the MigrateLib pipeline, which leverages a large language model (LLM) to (a) identify API calls tied to the source library, (b) generate equivalent calls for the target library, and (c) produce a diff of the required code changes.
2. VS Code extension architecture:
   • Client UI (webview) presents a migration wizard, letting users pick source/target libraries and view suggested edits.
   • Server component invokes the LLM via the existing MigrateLib service, streams back diffs, and applies accepted patches using VS Code’s workspace edit API.
3. User study design: 12 participants (mix of students and professional developers) performed two migration tasks—one with the CLI tool and one with MigMate—while the authors recorded task completion time, error rate, and SUS responses.

Results & Findings

• Time reduction: On average, MigMate cut migration time by ≈30 % compared with the CLI approach (median 4.2 min vs. 6.1 min).
• Usability: The extension achieved a SUS score of 84.5, placing it in the “excellent” range.
• Error handling: Participants reported fewer post‑migration build failures when using MigMate, thanks to the interactive review step.
• Developer confidence: Survey comments highlighted that seeing the exact diff inside the editor made users feel more in control of the automated changes.

Practical Implications

• Faster deprecation handling: Teams can automate the tedious process of swapping out libraries that reach end‑of‑life, keeping codebases secure and up‑to‑date.
• Reduced context switching: By staying inside VS Code, developers avoid the mental overhead of moving between terminal, editor, and external diff tools.
• Customizable migration pipelines: Because MigMate is open source, organizations can plug in their own LLM endpoints, add company‑specific migration rules, or extend support to other languages.
• Lower barrier to LLM adoption: The plugin demonstrates a concrete, low‑risk use case for LLMs in everyday coding tasks, encouraging broader experimentation in CI/CD pipelines or code‑review bots.

Limitations & Future Work

• Study scale: The user evaluation involved a modest number of participants and a limited set of migration scenarios, so results may not generalize to large, monolithic codebases.
• LLM dependency: Migration quality hinges on the underlying LLM’s knowledge of both source and target APIs; obscure or newly released libraries may yield sub‑optimal suggestions.
• Language focus: Currently limited to Python; extending to other ecosystems (JavaScript, Java, etc.) will require additional training data and prompt engineering.
• Future directions: The authors plan to (a) integrate automated test‑suite validation post‑migration, (b) support batch migrations across multiple repositories, and (c) explore fine‑tuning the LLM on domain‑specific code to improve accuracy.

Authors

• Matthias Kebede
• May Mahmoud
• Mohayeminul Islam
• Sarah Nadi

Paper Information

• arXiv ID: 2603.01596v1
• Categories: cs.SE
• Published: March 2, 2026
• PDF: Download PDF