Hashline vs Replace: Does the Edit Format Matter?
The article explores the performance of hashline-style edits (line-number anchored) vs. traditional replace-mode edits (old_string/new_string matching) for coding agents across multiple languages and models.
Why it matters
The findings provide insights into the practical considerations for building coding assistants that can effectively edit code across different languages and models.
Key Points
- 1Hashline vs replace is not a clear winner - the effect is language and model dependent
- 2Can's previous results on JavaScript are hard to generalize to other languages and setups
- 3Fuzzy matching is not a problem for current models - they either reproduce source text exactly or hallucinate completely different content
- 4Edit format is not the bottleneck - model selection and prompt engineering are more important factors
Details
The author built 'edit-bench' to test the performance of hashline-style edits vs. replace-mode edits across Python, TypeScript, and Rust codebases, using models like GPT-4.1-mini, Google Gemini-3, and Qwen3.5-397b. The results show that hashline hurts performance in Python, is roughly neutral in TypeScript and Rust, and the effect is model-dependent. The author also found that fuzzy matching (trim cascade) does not help in cases where the models get the 'old_string' wrong. Overall, the gap between model performance (90%+ for Gemini-3 vs 55-65% for GPT-4.1-mini) is much larger than the gap between edit formats, suggesting that investing in model selection and prompt engineering is more important than worrying about edit format.
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