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AceCoder: Utilizing Existing Code to Enhance Code Generation

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arxiv 2303.17780 v3 pith:7SMXK362 submitted 2023-03-31 cs.SE cs.AI

Jia Li , Yunfei Zhao , Yongmin Li , Ge Li , Zhi Jin This is my paper · ORCID
classification cs.SEcs.AI
keywords codeacecodergenerationllmspromptingchallengesdifferentexample
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. A key question is how to make prompts (i.e., Prompting Techniques). Existing prompting techniques are designed for natural language generation and have low accuracy in code generation. In this paper, we propose a new prompting technique named AceCoder. Our motivation is that code generation meets two unique challenges (i.e., requirement understanding and code implementation). AceCoder contains two novel mechanisms (i.e., guided code generation and example retrieval) to solve these challenges. (1) Guided code generation asks LLMs first to analyze requirements and output an intermediate preliminary (e.g., test cases). The preliminary is used to clarify requirements and tell LLMs "what to write". (2) Example retrieval selects similar programs as examples in prompts, which provide lots of relevant content (e.g., algorithms, APIs) and teach LLMs "how to write". We apply AceCoder to three LLMs (e.g., Codex) and evaluate it on three public benchmarks using the Pass@k. Results show that AceCoder can significantly improve the performance of LLMs on code generation. (1) In terms of Pass@1, AceCoder outperforms the state-of-the-art baseline by up to 56.4% in MBPP, 70.7% in MBJP, and 88.4% in MBJSP. (2) AceCoder is effective in LLMs with different sizes (i.e., 6B to 13B) and different languages (i.e., Python, Java, and JavaScript). (3) Human evaluation shows human developers prefer programs from AceCoder.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    RepoReasoner is a repository-level code-reasoning benchmark with output-prediction and call-chain tasks; the best LLM reaches only 69.1% Pass@1 even with oracle context, with low recall in dependency tracing.

  2. Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion

    cs.SE 2026-01 conditional novelty 6.0 of 10

    LLM-generated ripgrep queries plus BM25 re-ranking and line-interval de-duplication outperform graph- and RL-based retrievers for repository-level code completion on CrossCodeEval and RepoEval-Updated.

  3. Knowledge-Enhanced Program Repair for Data Science Code

    cs.SE 2025-02 conditional novelty 6.0 of 10

    DSrepair combines a knowledge graph of data science APIs with AST-level bug localization to repair LLM-generated code, fixing more DS-1000 tasks than five baseline repair methods.

  4. SABER: Model-agnostic Backdoor Attack on Chain-of-Thought in Neural Code Generation

    cs.SE 2024-12 reject novelty 6.0 of 10

    SABER poisons a chain-of-thought generator so that emphasized trigger words in a prompt make AI coding assistants write subtly incorrect code.

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    GRACE combines a multi-level code graph, hybrid text-structure retrieval, and graph fusion to improve repository-level code completion over vanilla and graph-based RAG baselines.

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