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[2602.21251] AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI
Summary
AgenticTyper is a novel AI-driven tool that automates the typing of legacy JavaScript projects, significantly reducing manual effort and enhancing type safety.
Why It Matters
As legacy JavaScript systems often lack type safety, maintaining them can be risky and costly. AgenticTyper addresses these challenges by automating type checking and error correction, which can streamline software maintenance and improve overall code quality, making it relevant for developers and organizations managing legacy codebases.
Key Takeaways
- AgenticTyper automates the typing process for legacy JavaScript projects.
- The tool resolves type errors significantly faster than manual methods.
- It enhances type safety and reduces maintenance risks in legacy systems.
- AgenticTyper utilizes LLMs for iterative error correction and behavior preservation.
- Evaluation showed complete resolution of initial type errors in just 20 minutes.
Computer Science > Software Engineering arXiv:2602.21251 (cs) [Submitted on 21 Feb 2026] Title:AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI Authors:Clemens Pohle View a PDF of the paper titled AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI, by Clemens Pohle View PDF HTML (experimental) Abstract:Legacy JavaScript systems lack type safety, making maintenance risky. While TypeScript can help, manually adding types is expensive. Previous automated typing research focuses on type inference but rarely addresses type checking setup, definition generation, bug identification, or behavioral correctness at repository scale. We present AgenticTyper, a Large Language Model (LLM)-based agentic system that addresses these gaps through iterative error correction and behavior preservation via transpilation comparison. Evaluation on two proprietary repositories (81K LOC) shows that AgenticTyper resolves all 633 initial type errors in 20 minutes, reducing manual effort from one working day. Comments: Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Programming Languages (cs.PL) ACM classes: D.2.3; D.2.7; F.3.3; I.2.2; I.2.11 Cite as: arXiv:2602.21251 [cs.SE] (or arXiv:2602.21251v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2602.21251 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Clemens Pohle [view email] [v1] Sat, 21 Feb 2026 17:53:3...
