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.

@inproceedings{abs-2602-21251,
  title = {AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI},
  author = {Clemens Pohle},
  booktitle = {ICSE},
  year = {2026},
  doi = {10.1145/3774748.3787746}
}