Automated Prompt Generation for Code Intelligence: An Empirical study and Experience in WeChat

• URL: http://arxiv.org/abs/2511.03136v1
• Date: Wed, 05 Nov 2025 02:59:51 GMT
• Title: Automated Prompt Generation for Code Intelligence: An Empirical study and Experience in WeChat
• Authors: Kexing Ji, Shiyun Fu, Cuiyun Gao, Yujia Chen, Zezhou Yang, Chaozheng Wang, Yuetang Deng,
• Abstract summary: Large Code Models (LCMs) show potential in code intelligence, but their effectiveness is greatly influenced by prompt quality.<n>While automated prompt generation (APG) exists in NLP, it is underexplored for code intelligence.<n>We propose a novel APG approach combining the best methods of the two parts.
• Score: 10.396978864444868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Code Models (LCMs) show potential in code intelligence, but their effectiveness is greatly influenced by prompt quality. Current prompt design is mostly manual, which is time-consuming and highly dependent on specific LCMs and tasks. While automated prompt generation (APG) exists in NLP, it is underexplored for code intelligence. This creates a gap, as automating the prompt process is essential for developers facing diverse tasks and black-box LCMs. To mitigate this, we empirically investigate two important parts of APG: Instruction Generation (IG) and Multi-Step Reasoning (MSR). IG provides a task-related description to instruct LCMs, while MSR guides them to produce logical steps before the final answer. We evaluate widely-used APG methods for each part on four open-source LCMs and three code intelligence tasks: code translation (PL-PL), code summarization (PL-NL), and API recommendation (NL-PL).Experimental results indicate that both IG and MSR dramatically enhance performance compared to basic prompts. Based on these results, we propose a novel APG approach combining the best methods of the two parts. Experiments show our approach achieves average improvements of 28.38% in CodeBLEU (code translation), 58.11% in ROUGE-L (code summarization), and 84.53% in SuccessRate@1 (API recommendation) over basic prompts. To validate its effectiveness in an industrial scenario, we evaluate our approach on WeChat-Bench, a proprietary dataset, achieving an average MRR improvement of 148.89% for API recommendation.

Related papers

• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning [106.98018881499362]
  We introduce GEPA (Genetic-Pareto), a prompt that thoroughly incorporates natural language to learn high-level rules from trial and error.<n>GEPA samples system-level trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems.<n>It can often turn even just a few rollouts into a large quality gain.
  arXiv  Detail & Related papers  (2025-07-25T17:42:32Z)
• A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat [16.059798732980347]
  Retrieval-augmented generation (RAG) has emerged as a promising method to enhance the code completion capabilities of large language models (LLMs)<n>We conduct an empirical study to investigate the performance of widely-used RAG methods for code completion in the industrial-scale of WeChat.
  arXiv  Detail & Related papers  (2025-07-24T15:36:31Z)
• Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code [76.80306464249217]
  We propose TeaR, which aims at teaching LLMs to reason better.<n>TeaR leverages careful data curation and reinforcement learning to guide models in discovering optimal reasoning paths through code-related tasks.<n>We conduct extensive experiments using two base models and three long-CoT distillation models, with model sizes ranging from 1.5 billion to 32 billion parameters, and across 17 benchmarks spanning Math, Knowledge, Code, and Logical Reasoning.
  arXiv  Detail & Related papers  (2025-07-10T07:34:05Z)
• Evaluating Large Language Models on Non-Code Software Engineering Tasks [4.381476817430934]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation.<n>We present the first comprehensive benchmark, which we name Software Engineering Language Understanding' (SELU)<n>SELU covers classification, regression, Named Entity Recognition (NER) and Masked Language Modeling (MLM) targets, with data drawn from diverse sources.
  arXiv  Detail & Related papers  (2025-06-12T15:52:32Z)
• Is Compression Really Linear with Code Intelligence? [60.123628177110206]
  textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
  arXiv  Detail & Related papers  (2025-05-16T16:59:14Z)
• Large Language Models as Code Executors: An Exploratory Study [29.545321608864295]
  This paper pioneers the exploration of Large Language Models (LLMs) as code executors. We are the first to examine this feasibility across various LLMs, including OpenAI's o1, GPT-4o, GPT-3.5, DeepSeek, and Qwen-Coder. We introduce an Iterative Instruction Prompting (IIP) technique that processes code snippets line by line, enhancing the accuracy of weaker models by an average of 7.22%.
  arXiv  Detail & Related papers  (2024-10-09T08:23:22Z)
• LLM Agents Improve Semantic Code Search [6.047454623201181]
  We introduce the approach of using Retrieval Augmented Generation powered agents to inject information into user prompts. By utilizing RAG, agents enhance user queries with relevant details from GitHub repositories, making them more informative and contextually aligned. Experimental results on the CodeSearchNet dataset demonstrate that RepoRift significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2024-08-05T00:43:56Z)
• Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond [24.151927600694066]
  Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
  arXiv  Detail & Related papers  (2024-04-23T08:24:43Z)
• Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge [85.17343729885003]
  We introduce Hint-before-Solving Prompting (HSP), which guides the model to generate hints for solving the problem. HSP can effectively improve the accuracy of reasoning tasks. We build the HSPMATH dataset based on HSP and fine-tuned Llemma-7B, reaching 64.3 accuracy.
  arXiv  Detail & Related papers  (2024-02-22T05:58:03Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

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.