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

Forum topic · 小凯 · 2026-09-29

Summary

This paper investigates whether natural-language documentation helps coding agents resolve software issues, and introduces tools to construct and evaluate such documentation. The authors propose a roundtrip benchmark that scores code descriptions by whether code regenerated from them passes the original tests, showing that completeness rather than length drives description fidelity. Using the benchmark as an optimization signal, they discover a description-writing prompt that achieves full fidelity and generalizes to unseen files. However, testing the motivating hypothesis—that better documentation improves agents' ability to resolve real repository issues—yields a negative result: across two model families and ten repositories, and with a positive control confirming the evaluation can detect genuine improvements, neither static compact documentation nor retrieved context outperforms providing only the issue itself. The paper reports this negative result, releases the benchmark and optimizer, and characterizes boundary conditions under which documentation may still help. Source: arXiv 2609.31587, authors Md Shohel Arman and Igor Molybog.

Paper Overview

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

What the Paper Does

The authors study whether natural-language documentation actually helps coding agents solve software issues, and build tools to construct and evaluate such documentation.

Key Contributions

1. Roundtrip benchmark: A code description is scored by whether code regenerated from that description passes the original tests. 2. Fidelity finding: Completeness, not length, drives a description's fidelity. 3. Optimized prompt: Using the benchmark as an optimization signal, the authors find a description-writing prompt that reaches full fidelity and generalizes to unseen files.

The Main (Negative) Result

The hypothesis motivating the work—that better documentation helps an agent resolve real repository issues—was tested across two model families and ten repositories, with a positive control confirming that the evaluation could detect genuine improvements. The result: it does not help. When the source code is available, neither static compact documentation nor retrieved context outperforms providing only the issue itself.

The authors report this negative result, release the benchmark and optimizer, and characterize the boundary conditions under which documentation can actually be useful.

Original Abstract (excerpt)

> 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.

*Auto-collected on 2026-09-29.*