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arxiv: 2604.15385 · v1 · submitted 2026-04-16 · 💻 cs.SE · cs.LG

Recognition: unknown

Prompt-Driven Code Summarization: A Systematic Literature Review

Afia Farjana , Zaiyu Cheng , Antonio Mastropaolo
Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:30 UTC · model grok-4.3

classification 💻 cs.SE cs.LG
keywords code summarizationprompt engineeringlarge language modelssystematic literature reviewsoftware documentationLLM promptingevaluation metrics
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The pith

A review of prompting techniques shows LLMs can generate better code summaries, but optimal strategies and evaluations remain unclear across studies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper conducts a systematic literature review to consolidate research on using large language models to create natural language summaries from source code. It notes that prompt design is critical to LLM success in this task, with approaches like few-shot examples, chain-of-thought reasoning, retrieval-augmented generation, and zero-shot learning offering improvements for software documentation. Yet the studies are scattered, leaving open questions about which techniques perform best for given models or conditions. Evaluation often relies on simple overlap metrics that miss deeper semantic accuracy, and the review maps these patterns while highlighting gaps for future work on reliable automated documentation.

Core claim

The central claim is that prompting paradigms such as few-shot prompting, chain-of-thought reasoning, retrieval-augmented generation, and zero-shot learning demonstrate promise for enhancing LLM performance on code summarization, yet existing research remains fragmented with limited insight into the best strategies for specific models and contexts, and most evaluations depend on overlap-based metrics that may fail to reflect semantic quality.

What carries the argument

Categorization of prompting paradigms (few-shot, zero-shot, chain-of-thought, retrieval-augmented) combined with cross-study analysis of their effectiveness and evaluation practices in LLM-driven code summarization.

If this is right

  • Researchers need targeted experiments to identify conditions under which particular prompting strategies outperform others for different models.
  • Development of metrics that capture semantic quality beyond simple overlap measures would improve assessment of summary usefulness.
  • Clearer guidelines on prompt design could support more consistent integration of automated summarization into developer workflows.
  • Filling identified gaps would reduce reliance on manual documentation and aid tasks such as code review and maintenance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If prompting best practices become standardized, integrated tools in development environments could produce more reliable documentation at scale.
  • Better code summaries from optimized prompts may indirectly improve accuracy in downstream applications like defect localization or commit message generation.
  • The current fragmentation points to a need for shared benchmark datasets that test prompting strategies across varied codebases and languages.

Load-bearing premise

The collected studies form a representative sample of the field and the chosen categorization of prompting paradigms accurately reflects the underlying technical distinctions without significant selection or reporting bias.

What would settle it

A new comprehensive survey that incorporates overlooked recent papers and finds either consistent superiority of one prompting method across models or evaluation results that align closely on semantic quality would contradict the fragmentation and limited-understanding conclusions.

Figures

Figures reproduced from arXiv: 2604.15385 by Afia Farjana, Antonio Mastropaolo, Zaiyu Cheng.

Figure 1
Figure 1. Figure 1: Evolution timeline of code summarization paradigms (2010–2025), based on Zhu & Pan [ [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Study selection process for the systematic review on prompt-based code summarization. The pipeline includes four stages: (i) identification [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Publication Year initial study corpus. For studies without author-supplied keywords, manual annotation was performed using title, abstract, and methodological content, following the guidelines of [77]. This process yielded 4 candidate papers for manual analysis. Both the Python script and the complete prompt-engineering terms used in our search are provided in our replication package. Thresholding rule: we… view at source ↗
Figure 4
Figure 4. Figure 4: Venue Distribution of Prompt Engineering Techniques in Code Summarization [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A Taxonomy of Prompt Engineering Techniques for Code Summarization Across Granularity Levels and Prompt Paradigms. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Trend of LLM family adoption across code-summarization studies (2020–2025). The trend highlights a gradual diversification of model [PITH_FULL_IMAGE:figures/full_fig_p027_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of programming languages across 29 prompt-based code summarization studies. Each ring represents a prompting paradigm [PITH_FULL_IMAGE:figures/full_fig_p029_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Artifact-sharing landscape across 29 studies. Preprocessing and training scripts are the most frequently shared artifacts, followed by [PITH_FULL_IMAGE:figures/full_fig_p036_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Year-wise availability of replication packages in primary studies (2020–2025). Bars show [PITH_FULL_IMAGE:figures/full_fig_p037_9.png] view at source ↗
read the original abstract

Software documentation is essential for program comprehension, developer onboarding, code review, and long-term maintenance. Yet producing quality documentation manually is time-consuming and frequently yields incomplete or inconsistent results. Large language models (LLMs) offer a promising solution by automatically generating natural language descriptions from source code, helping developers understand code more efficiently, facilitating maintenance, and supporting downstream activities such as defect localization and commit message generation. However, the effectiveness of LLMs in documentation tasks critically depends on how they are prompted. Properly structured instructions can substantially improve model performance, making prompt engineering-the design of input prompts to guide model behavior-a foundational technique in LLM-based software engineering. Approaches such as few-shot prompting, chain-of-thought reasoning, retrieval-augmented generation, and zero-shot learning show promise for code summarization, yet current research remains fragmented. There is limited understanding of which prompting strategies work best, for which models, and under what conditions. Moreover, evaluation practices vary widely, with most studies relying on overlap-based metrics that may not capture semantic quality. This systematic literature review consolidates existing evidence, categorizes prompting paradigms, examines their effectiveness, and identifies gaps to guide future research and practical adoption.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. This paper presents a systematic literature review on prompt-driven code summarization with large language models. It consolidates evidence on prompting paradigms (few-shot prompting, chain-of-thought reasoning, retrieval-augmented generation, and zero-shot learning), examines their effectiveness for generating natural language code descriptions, notes that research remains fragmented with limited understanding of optimal strategies/models/conditions, critiques reliance on overlap-based evaluation metrics, and identifies gaps to guide future work.

Significance. If methodologically rigorous, this SLR would be a useful consolidation in an active area of LLM applications for software engineering. It would help clarify promising prompting directions, surface evaluation weaknesses, and reduce fragmentation by mapping what is known about prompt effectiveness for code summarization tasks that support comprehension, maintenance, and downstream activities.

major comments (1)
  1. [Methodology section] Methodology section: The central synthesis—that prompting approaches show promise yet research is fragmented with limited understanding of best strategies—depends on the collected studies forming a representative sample and the chosen categorization (few-shot, CoT, RAG, zero-shot) accurately reflecting technical distinctions without selection or reporting bias. The abstract outlines scope but provides no explicit search protocol, databases, inclusion/exclusion criteria, or quality assessment details, leaving these load-bearing elements unverified and risking incomplete coverage or forced groupings that undermine the gap analysis.
minor comments (1)
  1. [Abstract] Abstract: The claim that 'most studies relying on overlap-based metrics' would be strengthened by stating the total number of primary studies reviewed and the covered time period.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our systematic literature review. We address the major comment on methodology below and will revise the manuscript accordingly to improve transparency and verifiability.

read point-by-point responses

  1. Referee: [Methodology section] Methodology section: The central synthesis—that prompting approaches show promise yet research is fragmented with limited understanding of best strategies—depends on the collected studies forming a representative sample and the chosen categorization (few-shot, CoT, RAG, zero-shot) accurately reflecting technical distinctions without selection or reporting bias. The abstract outlines scope but provides no explicit search protocol, databases, inclusion/exclusion criteria, or quality assessment details, leaving these load-bearing elements unverified and risking incomplete coverage or forced groupings that undermine the gap analysis.

    Authors: We agree that the abstract does not explicitly detail the search protocol, databases, inclusion/exclusion criteria, or quality assessment, which limits immediate verifiability for readers. The full manuscript contains a dedicated Methodology section (Section 3) that outlines a structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and arXiv using predefined keyword strings (detailed in Appendix A), with inclusion criteria limited to empirical studies (2020 onward) evaluating LLM prompting for code summarization and exclusion of non-empirical or non-English works. Quality assessment used a modified Kitchenham checklist with reported inter-rater reliability. The four-category taxonomy was derived from the primary technique reported in each primary study, with dual-author independent coding and consensus resolution to mitigate bias. However, we acknowledge the referee's point that these elements could be presented more explicitly and with a PRISMA diagram to strengthen the synthesis. We will revise the abstract to include a concise methodology summary and expand Section 3 with additional justification for the categorization scheme and search coverage. revision: yes

Circularity Check

0 steps flagged

No circularity: SLR aggregates external studies without self-referential derivations

full rationale

This systematic literature review consolidates evidence from external papers on prompting strategies for code summarization. It contains no mathematical derivations, fitted parameters, predictions, or uniqueness theorems that reduce to the paper's own inputs by construction. Claims about fragmentation and promising approaches (few-shot, CoT, RAG, zero-shot) are synthesized from cited literature rather than defined or forced by the review's own categorization or search process. No self-citation chains or ansatzes are load-bearing for the central synthesis. The paper is self-contained against external benchmarks as a review.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities. The review rests on the domain assumption that established systematic literature review procedures can reliably map a fragmented research area.

axioms (1)
  • domain assumption Standard systematic literature review methodology (search strategy, inclusion criteria, quality assessment) is sufficient to consolidate evidence without major bias
    Invoked implicitly by the decision to perform an SLR and to draw conclusions about effectiveness and gaps.

pith-pipeline@v0.9.0 · 5508 in / 1256 out tokens · 91559 ms · 2026-05-10T11:30:51.383645+00:00 · methodology

discussion (0)

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Reference graph

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117 extracted references · 39 canonical work pages · 11 internal anchors

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