# Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations

arXiv: https://arxiv.org/abs/2602.03400 · HTML: https://mlsanity.com/paper/2602.03400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 69 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 30 of 30)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (2023)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation](https://mlsanity.com/paper/2308.01240/) (2023)
- [No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence](https://mlsanity.com/paper/2207.11680/) (2022)
- [Automatic Code Documentation Generation Using GPT-3](https://mlsanity.com/paper/2209.02235/) (2022)
- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [Automatic Code Summarization: A Systematic Literature Review](https://mlsanity.com/paper/1909.04352/) (2019)
- [EditSum: A Retrieve-and-Edit Framework for Source Code Summarization](https://mlsanity.com/paper/2308.13775/) (2023)
- [Developer-Intent Driven Code Comment Generation](https://mlsanity.com/paper/2302.07055/) (2023)
- [Prompt-Reverse Inconsistency: LLM Self-Inconsistency Beyond Generative Randomness and Prompt Paraphrasing](https://mlsanity.com/paper/2504.01282/) (2025)
- [GenSelect: A Generative Approach to Best-of-N](https://mlsanity.com/paper/2507.17797/) (2025)
- [Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution](https://mlsanity.com/paper/2206.11815/) (2022)
- [Can Large Language Models Serve as Evaluators for Code Summarization?](https://mlsanity.com/paper/2412.01333/) (2024)
- [API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations](https://mlsanity.com/paper/2103.10668/) (2021)
- [Unsupervised Lexical Substitution with Decontextualised Embeddings](https://mlsanity.com/paper/2209.08236/) (2022)
- [Evaluation of Large Language Models via Coupled Token Generation](https://mlsanity.com/paper/2502.01754/) (2025)
- [A Decade of Code Comment Quality Assessment: A Systematic Literature Review](https://mlsanity.com/paper/2209.08165/) (2022)
- [Large Language Models for Code Summarization](https://mlsanity.com/paper/2405.19032/) (2024)
- [Deep Learning to Detect Redundant Method Comments](https://mlsanity.com/paper/1806.04616/) (2018)
- [What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk](https://mlsanity.com/paper/2005.11583/) (2020)

## Abstract (arXiv)

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.