https://arxiv.org/abs/2412.18573 Change to arXiv's privacy policy The arXiv Privacy Policy has changed. By continuing to use arxiv.org, you are agreeing to the privacy policy. I Understand Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2412.18573 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Software Engineering arXiv:2412.18573 (cs) [Submitted on 24 Dec 2024] Title:How Well Do LLMs Generate Code for Different Application Domains? Benchmark and Evaluation Authors:Dewu Zheng, Yanlin Wang, Ensheng Shi, Hongyu Zhang, Zibin Zheng View a PDF of the paper titled How Well Do LLMs Generate Code for Different Application Domains? Benchmark and Evaluation, by Dewu Zheng and 4 other authors View PDF HTML (experimental) Abstract:Recently, an increasing number of AI-driven programming assistants powered by code LLMs have been integrated into various real-world software development environments, significantly boosting developer productivity. However, existing code generation benchmarks primarily focus on general-purpose scenarios, leaving the code generation performance of LLMs for specific application domains largely unknown. In this paper, we introduce a new benchmark, MultiCodeBench, to fill this gap. MultiCodeBench comprises 2,400 programming tasks, covering 12 popular software development domains and 15 programming languages. Specifically, we perform in-depth research to identify these 12 application domains. Given that each domain may involve multiple technical frameworks, and that different frameworks present distinct challenges in the coding process, we categorize the commonly used frameworks and platforms within each domain. We then sample programming problems from GitHub repositories related to these subdomains. To ensure the quality of the tasks and mitigate data leakage issues, we invite annotators to rewrite the docstrings for each task in MultiCodeBench. Additionally, we build a static analysis-based dependency parsing tool to extract the dependencies in the ground truth for each task, enabling deeper performance analysis. Through extensive experiments on MultiCodeBench with eleven representative mainstream LLMs, we reveal the code generation performance of the LLMs across different application domains, providing practical insights for developers in downstream fields when selecting LLMs. Furthermore, we analyze the reasons behind the models' failures in completing software application development tasks, offering guidance for model developers to enhance domain-specific code generation capabilities. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2412.18573 [cs.SE] (or arXiv:2412.18573v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2412.18573 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dewu Zheng [view email] [v1] Tue, 24 Dec 2024 17:56:08 UTC (410 KB) Full-text links: Access Paper: View a PDF of the paper titled How Well Do LLMs Generate Code for Different Application Domains? Benchmark and Evaluation, by Dewu Zheng and 4 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.SE < prev | next > new | recent | 2024-12 Change to browse by: cs cs.AI cs.CL References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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