Is Reasoning All You Need? An Empirical Study of Retrieval and Structured Reasoning for Python Class-Level Docstring Generation

ACL ARR 2026 January Submission1151 Authors

Keywords: Retrieval Augmented Generations, Python Docstring Generation, Large Language Models, Code Explainability, Chain of Thought, Tree of Thought, Graph of Thought

Abstract:

Automated docstring generation is a high-fidelity software engineering task where factual correctness, interface coverage, and efficiency are critical. Recent work increasingly applies generation-time reasoning strategies—such as Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)—yet their benefits relative to retrieval-based grounding for code documentation remain unclear. We present a controlled empirical evaluation of 12 Python class-level docstring generation strategies, combining three architectural families—Plain LLM, Retrieval-Augmented Generation (RAG), and Iterative Critique RAG—with four reasoning modes: Base, CoT, ToT, and GoT. Strategies are evaluated using lexical and semantic metrics (ROUGE, BLEU, BERTScore), code-specific coverage measures, an LLM-as-a-Judge faithfulness metric, and latency-based cost analysis.

Our results reveal three consistent findings. First, retrieval is the primary driver of factual reliability: a simple RAG baseline achieves 73\% faithfulness, compared to 50\% for Plain LLMs and 57\% for Iterative Critique RAG (+46\% relative improvement). Second, reasoning-heavy strategies show diminishing returns: ToT and GoT increase latency by up to 15× without statistically significant gains in faithfulness or semantic similarity ($\leq$ +2-3\% BERTScore). Third, SimpleRAG offers the best cost–quality trade-off, generating high-quality docstrings in ~6 seconds versus ~90 seconds for Tree-of-Thought pipelines. Overall, our findings indicate that for Python class-level docstring generation—a static, context-bound task—efficient retrieval-based grounding is more effective than generation-time reasoning. We release a reproducible, cost-aware benchmark to support principled evaluation of automated documentation systems.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: code generation and understanding

Contribution Types: Model analysis & interpretability

Languages Studied: Python

Submission Number: 1151