mlsanityData to 7 Oct 2026

On Evaluating the Efficiency of Source Code Generated by LLMs

arXiv 2404.06041 ↗First posted 9 Apr 2024cs.SEcited by 19

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Abstract (arXiv)

Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code generated by LLMs, we propose to further evaluate its efficiency. More efficient code can lead to higher performance and execution efficiency of programs and software completed by LLM-assisted programming. First, we evaluate the efficiency of the code generated by LLMs on two benchmarks, HumanEval and MBPP. Then, we choose a set of programming problems from the online judge platform LeetCode to conduct a more difficult evaluation. Finally, we explore several prompts that would enable LLMs to generate more efficient code.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 9; 2026: 3.

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  1. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHubcited by 53
  2. 2024How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmarkcited by 39
  3. 2024EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimizationcited by 28
  4. 2024ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?cited by 25
  5. 2025EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Codecited by 24
  6. 2025GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agentscited by 24
  7. 2024EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuningcited by 15
  8. 2025LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctnesscited by 10
  9. 2024Measuring the Influence of Incorrect Code on Test Generationcited by 9
  10. 2024A Problem-Oriented Perspective and Anchor Verification for Code Optimizationcited by 3
  11. 2024Generating Energy-efficient code with LLMscited by 3
  12. 2025Evaluating the Energy-Efficiency of the Code Generated by LLMscited by 3
7 more
  1. 2025PerfCoder: Large Language Models for Interpretable Code Performance Optimizationcited by 3
  2. 2025A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standardscited by 1
  3. 2025Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programmingcited by 1
  4. 2026Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Codecited by 1
  5. 2026Unlocking LLM Code Correction with Iterative Feedback Loopscited by 1
  6. 2025Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Softwarecited by 0
  7. 2026Rethinking Code Performance Benchmarks for LLMscited by 0