Compositional API Recommendation for Library-Oriented Code Generation
<|reference_start|>Compositional API Recommendation for Library-Oriented Code Generation: Large language models (LLMs) have achieved exceptional performance in code generation. However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs. Previous work utilizes API recommendation technology to help LLMs use libraries: it retrieves APIs related to the user requirements, then leverages them as context to prompt LLMs. However, developmental requirements can be coarse-grained, requiring a combination of multiple fine-grained APIs. This granularity inconsistency makes API recommendation a challenging task. To address this, we propose CAPIR (Compositional API Recommendation), which adopts a "divide-and-conquer" strategy to recommend APIs for coarse-grained requirements. Specifically, CAPIR employs an LLM-based Decomposer to break down a coarse-grained task description into several detailed subtasks. Then, CAPIR applies an embedding-based Retriever to identify relevant APIs corresponding to each subtask. Moreover, CAPIR leverages an LLM-based Reranker to filter out redundant APIs and provides the final recommendation. To facilitate the evaluation of API recommendation methods on coarse-grained requirements, we present two challenging benchmarks, RAPID (Recommend APIs based on Documentation) and LOCG (Library-Oriented Code Generation). Experimental results on these benchmarks, demonstrate the effectiveness of CAPIR in comparison to existing baselines. Specifically, on RAPID's Torchdata-AR dataset, compared to the state-of-the-art API recommendation approach, CAPIR improves recall@5 from 18.7% to 43.2% and precision@5 from 15.5% to 37.1%. On LOCG's Torchdata-Code dataset, compared to code generation without API recommendation, CAPIR improves pass@100 from 16.0% to 28.0%.<|reference_end|>
@article{ma2024compositional,
    title={Compositional API Recommendation for Library-Oriented Code Generation},
    author={Zexiong Ma, Shengnan An, Bing Xie, Zeqi Lin},
    journal={32nd IEEE/ACM International Conference on Program Comprehension
  (ICPC 2024), Apr 2024, Lisboa, Portugal},
    year={2024},
    doi={10.1145/3643916.3644403},
    archivePrefix={arXiv},
    eprint={2402.19431},
    primaryClass={cs.SE cs.AI cs.CL}
}