Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations
- URL: http://arxiv.org/abs/2602.03400v1
- Date: Tue, 03 Feb 2026 11:22:28 GMT
- Title: Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations
- Abstract summary: We propose ExpSum, an expectation-aware code summarization approach.<n>We evaluate ExpSum on the HarmonyOS project and widely used code summarization benchmarks.
- Score: 2.508240448634342
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Code summaries are essential for helping developers understand code functionality and reducing maintenance and collaboration costs. Although recent advances in large language models (LLMs) have significantly improved automatic code summarization, the practical usefulness of generated summaries in industrial settings remains insufficiently explored. In collaboration with documentation experts from the industrial HarmonyOS project, we conducted a questionnaire study showing that over 57.4% of code summaries produced by state-of-the-art approaches were rejected due to violations of developers' expectations for industrial documentation. Beyond semantic similarity to reference summaries, developers emphasize additional requirements, including the use of appropriate domain terminology, explicit function categorization, and the avoidance of redundant implementation details. To address these expectations, we propose ExpSum, an expectation-aware code summarization approach that integrates function metadata abstraction, informative metadata filtering, context-aware domain knowledge retrieval, and constraint-driven prompting to guide LLMs in generating structured, expectation-aligned summaries. We evaluate ExpSum on the HarmonyOS project and widely used code summarization benchmarks. Experimental results show that ExpSum consistently outperforms all baselines, achieving improvements of up to 26.71% in BLEU-4 and 20.10% in ROUGE-L on HarmonyOS. Furthermore, LLM-based evaluations indicate that ExpSum-generated summaries better align with developer expectations across other projects, demonstrating its effectiveness for industrial code documentation.
Related papers
- Progressive Code Integration for Abstractive Bug Report Summarization [0.32004175453646166]
We propose a progressive code-integration framework for abstractive bug report summarization.<n>Our approach incrementally incorporates long code snippets alongside textual content.<n>Our pipeline outperforms extractive baselines by 7.5%-58.2% and achieves performance comparable to state-of-the-art abstractive methods.
arXiv Detail & Related papers (2025-11-29T05:35:36Z) - RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback [87.97664892075811]
We present RECODE-H, a benchmark of 102 tasks from research papers and repositories.<n>It includes structured instructions,unit tests, and a five-level feedback hierarchy to reflect realistic researcher-agent collaboration.<n>We also present ReCodeAgent, a framework that integrates feedback into iterative code generation.
arXiv Detail & Related papers (2025-10-07T17:45:35Z) - What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond [32.467437657603604]
Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts.<n>We propose AllianceCoder, a novel context-integrated method that employs chain-of-thought prompting to decompose user queries into implementation steps and retrieves APIs via semantic description matching.<n>Through extensive experiments on CoderEval and RepoExec, AllianceCoder achieves state-of-the-art performance, improving Pass@1 by up to 20% over existing approaches.
arXiv Detail & Related papers (2025-03-26T14:41:38Z) - Code Summarization Beyond Function Level [0.213063058314067]
This study investigated the effectiveness of code summarization models beyond the function level.<n>The fine-tuned state-of-the-art CodeT5+ base model excelled in code summarization.<n> Repository-level summarization exhibited promising potential but requires significant computational resources.
arXiv Detail & Related papers (2025-02-23T20:31:21Z) - Intention is All You Need: Refining Your Code from Your Intention [19.827036493004435]
This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process.<n>Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation.<n>Our approach achieves 79% accuracy in intention extraction and up to 66% in code refinement generation.
arXiv Detail & Related papers (2025-02-12T07:26:13Z) - OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
arXiv Detail & Related papers (2024-11-07T17:47:25Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback.
Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
arXiv Detail & Related papers (2024-07-05T20:25:04Z) - LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge.<n>We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
arXiv Detail & Related papers (2024-05-07T04:00:30Z) - The All-Seeing Project V2: Towards General Relation Comprehension of the Open World [58.40101895719467]
We present the All-Seeing Project V2, a new model and dataset designed for understanding object relations in images.
We propose the All-Seeing Model V2 that integrates the formulation of text generation, object localization, and relation comprehension into a relation conversation task.
Our model excels not only in perceiving and recognizing all objects within the image but also in grasping the intricate relation graph between them.
arXiv Detail & Related papers (2024-02-29T18:59:17Z) - REPOFUSE: Repository-Level Code Completion with Fused Dual Context [11.531678717514724]
This paper introduces REPOFUSE, a pioneering solution designed to enhance repository-level code completion without the latency trade-off.
We propose a novel rank truncated generation (RTG) technique that efficiently condenses two types of context into prompts with restricted size.
REPOFUSE has demonstrated a significant leap over existing models, achieving a 40.90% to 59.75% increase in exact match (EM) accuracy for code completions and a 26.8% enhancement in inference speed.
arXiv Detail & Related papers (2024-02-22T06:34:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.