Compact Documentation for Coding Agents: A Roundtrip Benchmark, an Optimizer, and a Negative Result

Forum topic · 小凯 · 2026-09-29

Summary

This paper investigates whether natural-language documentation actually helps coding agents resolve real software issues. The authors introduce a roundtrip benchmark that scores code descriptions by whether code regenerated from those descriptions passes the original unit tests, showing that completeness rather than length determines a description's fidelity. Using this benchmark as an optimization signal, they discover a description-writing prompt that achieves full fidelity and generalizes to unseen files. They then test the motivating hypothesis that better documentation improves issue resolution. Across two model families and ten repositories, with a positive control confirming the evaluation can detect genuine improvements, they find it does not: when source code is available, neither compact static documentation nor retrieved context outperforms providing the issue alone. The authors report this negative result, release the benchmark and optimizer, and characterize boundary conditions where documentation may still help. Paper: arXiv:2609.31587.

Paper Overview

Field: NLP
Authors: Md Shohel Arman, Igor Molybog
Published: 2026-09-25
arXiv: 2609.31587

Summary (translated from the Chinese post)

The authors study whether natural-language documentation genuinely helps coding agents solve software problems, and build tools to construct and evaluate such documentation. They propose a roundtrip benchmark: code descriptions are scored by whether code regenerated from the description passes the original tests. They show that completeness, not length, drives a description's fidelity.

Using the benchmark as an optimization signal, they find a description-writing prompt that achieves full fidelity and generalizes to unseen files. They then test the hypothesis that motivated the work: that better documentation helps agents resolve issues in real repositories.

Key finding — a negative result: Across two model families and ten repositories, with a positive control confirming the evaluation can detect genuine improvements, documentation does not help. When source code is available, neither static compact documentation nor retrieved context outperforms providing the issue alone.

The paper reports this negative result, releases the benchmark and optimizer, and characterizes the boundary conditions under which documentation can genuinely help.

Abstract (original)

> We investigate whether natural-language documentation helps coding agents resolve software issues, and we build the tools to construct and evaluate it. We introduce a roundtrip benchmark that scores code descriptions by whether code regenerated from them passes the original tests, and show that completeness, not length, drives a description's fidelity. Using the benchmark as an optimization signal, we discover a description-writing prompt that reaches full fidelity and generalizes to unseen files. We then test the hypothesis that motivated the work: that better documentation helps an agent resolve real repository issues. Across two model families and ten repositories, and against a positive control confirming that our evaluation can detect a genuine improvement, we find that it does not. W...

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