Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark (2412.18573v2)

Abstract: With the rapid advancement of LLMs, extensive research has been conducted to investigate the code generation capabilities of LLMs. However, existing efforts primarily focus on general-domain tasks, leaving LLMs' code generation performance in real-world application domains underexplored. This raises a critical question: can a model's general-domain coding ability reliably represent its ability in specialized domains? In this paper, we introduce DomainCodeBench, a multi-domain code generation benchmark designed to systematically evaluate LLMs across 12 software application domains and 15 programming languages. DomainCodeBench contains 2,400 manually verified tasks with ground truth, human-annotated docstrings, and fine-grained dependency information to ensure more coverage of domain-specific challenges. Specifically, we first identify the most popular application domains by topic mining. Then, we curate coding tasks based on commonly used frameworks and platforms in each domain. We obtain several findings through extensive experiments on DomainCodeBench with ten mainstream LLMs. (1) Performance decoupling: experiments reveal that top general-domain models do not consistently excel in specific application domains; (2) Domain-specific weaknesses: LLMs often fail due to domain knowledge gaps and third-party library misusage; (3) Contextual enhancement: we show that augmenting prompts with domain-specific knowledge improves performance by around 38.17%, providing actionable insights for performance optimization. Our replication package, including the benchmark, source code, and experimental results, is available at https://github.com/DeepSoftwareAnalytics/DomainCodeBench.

• The paper introduces MultiCodeBench, a new benchmark with 2,400 tasks across 12 domains and 15 languages, to evaluate LLM performance in domain-specific code generation.
• Experimental results show marked variability in LLM performance across domains, with no direct correlation between model size and domain-specific capability.
• Providing domain-specific context significantly improves LLM performance, highlighting the need for models to better handle repository contexts and specialized domain knowledge.

Evaluation of LLMs in Domain-Specific Code Generation: Insights from MultiCodeBench

The paper "How Well Do LLMs Generate Code for Different Application Domains? Benchmark and Evaluation" offers a comprehensive examination of the efficacy of LLMs for domain-specific code generation tasks. It introduces a new benchmark called MultiCodeBench, designed to evaluate the performance of these models across diverse software application domains and programming languages. This essay provides an analytical overview of the paper's contributions, methodological approach, and implications for further research and application.

Overview of Contributions

The paper addresses a significant gap in current research: the paucity of evaluations focusing on domain-specific code generation by LLMs. Existing benchmarks predominantly assess models on general-purpose programming tasks, thus neglecting the nuanced challenges and requirements associated with specific application domains. MultiCodeBench is introduced to fill this void, presenting a robust benchmark encompassing 2,400 task instances across 12 software domains and 15 programming languages. This extensive framework facilitates a multifaceted analysis of LLM performance in various real-world domains, ranging from blockchain to robotics and web development, among others.

Methodology

The authors employ a methodical approach to construct MultiCodeBench. They begin by identifying popular application domains based on discourse within tech communities and subsequently categorize these domains into subdomains characterized by specific frameworks and platforms. This classification ensures that the benchmark captures a wide array of domain-specific challenges.

To populate MultiCodeBench with tasks, the authors selectively sample from high-quality GitHub repositories relevant to each domain, thereby ensuring the practical relevance and complexity of the tasks. Importantly, they engage domain-experienced annotators to rewrite docstrings for each task, thereby mitigating data leakage concerns and maintaining the accuracy of requirement descriptions. Additionally, the paper emphasizes the importance of dependency analysis, utilizing static analysis to extract pertinent dependency information which aids LLMs in navigating complex project structures.

Experiments and Results

The authors conduct extensive experiments with eleven mainstream LLMs, both open-source and closed-source, including prominent models like GPT-4 and CodeLLaMa series. The evaluation metric employed is CodeBLEU, enabling a nuanced assessment of code generation quality across domains. The results reveal some unexpected insights:

1. Domain-Specific Performance Variability: There is a marked variability in LLM performance across different domains. For instance, LLMs perform relatively well in blockchain and mobile application development but struggle in web and enterprise application development. This highlights the limitations of general-purpose benchmarks in predicting performance in specific domains.
2. Parameter Scale and Model Performance: The paper finds no direct correlation between model size and domain-specific performance, challenging assumptions that larger models inherently provide superior performance.
3. Contextual Information and Performance: The provision of domain-specific context, including import statements and third-party library dependencies, significantly enhances LLM performance, underscoring the necessity of contextual understanding in complex software environments.

Implications and Future Directions

The paper's findings have several implications for both LLM users and developers. For practitioners integrating these models into domain-specific tasks, the results suggest cautious optimism; while LLMs have promising potential, their effectiveness hinges on the careful selection and contextual understanding of tasks. For model developers, the highlighted deficiencies point to areas ripe for improvement, such as enhancing models' comprehension of repository contexts and specialized domain knowledge.

Looking forward, the MultiCodeBench framework sets a precedent for future research into domain-specific LLM deployment. It prompts a reevaluation of how LLMs are assessed, advocating for a shift towards real-world application scenarios. The framework also opens avenues for exploring adaptive strategies in LLM deployment, where models can dynamically leverage external knowledge bases to mitigate context comprehension limitations.

Conclusion

In summary, the paper makes significant strides in evaluating LLMs within the context of specific application domains, offering the research community a detailed benchmark that accounts for the complex and varied nature of real-world programming environments. By addressing the gaps in current evaluation methodologies, MultiCodeBench not only provides critical insights into current model capabilities but also lays the groundwork for future developments in AI-assisted software engineering.