A Survey on Large Language Models for Code Generation

Analysis

Why This Paper Matters

Large Language Models (LLMs) have rapidly transformed code generation, with tools like GitHub Copilot bringing AI-assisted programming into mainstream software development. Despite this surge of interest from both academia and industry, the field has lacked a comprehensive and up-to-date literature review dedicated specifically to LLMs for code generation. This survey fills that critical gap by systematically organizing the burgeoning research landscape, providing a structured reference for newcomers and seasoned researchers alike.

The paper's significance is further amplified by its dual focus on both Natural Language Processing (NLP) and Software Engineering (SE) perspectives, acknowledging that code generation sits at the intersection of these disciplines. By introducing a taxonomy that spans data curation, model advances, evaluation, ethics, environmental impact, and real-world applications, the authors offer a holistic view that goes beyond mere model performance. This is essential for practitioners who need to navigate trade-offs in deploying code LLMs responsibly and effectively.

Moreover, the survey's commitment to ongoing updates via a dedicated GitHub repository ensures its continued relevance in a fast-moving field. The comparative analysis of experimental results across HumanEval, MBPP, and BigCodeBench provides a quantitative snapshot of progress, helping to identify state-of-the-art approaches and benchmark-specific trends. This makes the survey not just a static overview but a living resource for the community.

Technical Contributions

• Comprehensive Taxonomy: The survey categorizes recent developments in code LLMs across multiple dimensions: data curation, latest advances, performance evaluation, ethical implications, environmental impact, and real-world applications. This taxonomy provides a structured framework for understanding the field.
• Historical Overview: It traces the evolution of LLMs for code generation, offering context for current advancements and highlighting key milestones.
• Comparative Benchmark Analysis: The authors conduct a quantitative and qualitative comparison of code LLMs on HumanEval, MBPP, and BigCodeBench, sourcing results from original papers to ensure fair comparison across difficulty levels and task types.
• Identification of Challenges and Opportunities: The survey pinpoints critical gaps between academia and practical development, outlining promising directions for future research.
• Living Resource: A dedicated GitHub page continuously documents and disseminates recent advances, ensuring the survey remains current.

Results

The survey reports a comparative analysis of experimental results from various code LLMs on three prominent benchmarks: HumanEval, MBPP, and BigCodeBench. While specific metrics are not detailed in the abstract, the analysis highlights progressive enhancements in LLM capabilities for code generation across different difficulty levels and programming task types. By sourcing results directly from original papers, the authors aim to provide a fair and consistent comparison, enabling readers to gauge the relative performance of models and identify trends. The inclusion of BigCodeBench, a more recent and challenging benchmark, underscores the survey's up-to-date coverage.

Significance

This survey is poised to become a key reference for researchers and practitioners working on code generation with LLMs. By consolidating scattered literature into a coherent taxonomy and providing comparative performance insights, it lowers the barrier to entry for new researchers and helps practitioners make informed decisions. The emphasis on ethical implications and environmental impact is particularly timely, as the AI community grapples with the responsible deployment of large models. Furthermore, by identifying the academia-industry gap, the survey encourages more applied research and collaboration, ultimately accelerating the development of more capable and reliable code generation systems. As code LLMs continue to evolve, this survey's living resource will play a vital role in keeping the community informed and aligned.