mlsanityData to 7 Oct 2026

Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust

arXiv 2405.13101 ↗First posted 21 May 2024cs.SE cs.AIcited by 5

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Abstract (arXiv)

This study evaluates the capabilities of ChatGPT versions 3.5 and 4 in generating code across a diverse range of programming languages. Our objective is to assess the effectiveness of these AI models for generating scientific programs. To this end, we asked ChatGPT to generate three distinct codes: a simple numerical integration, a conjugate gradient solver, and a parallel 1D stencil-based heat equation solver. The focus of our analysis was on the compilation, runtime performance, and accuracy of the codes. While both versions of ChatGPT successfully created codes that compiled and ran (with some help), some languages were easier for the AI to use than others (possibly because of the size of the training sets used). Parallel codes -- even the simple example we chose to study here -- also difficult for the AI to generate correctly.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 21 May 2024.

Builds on

9 of 23 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 150
  4. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 97
  5. 2023Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issuescited by 78
  6. 2023AI-Generated Content (AIGC): A Surveycited by 59
  7. 2023A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutionscited by 34
  8. 2023Practices and Challenges of Using GitHub Copilot: An Empirical Studycited by 13
  9. 2023Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Javacited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Taskscited by 8
  2. 2025LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languagescited by 5
  3. 2025Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++cited by 3
  4. 2026GTBench: A Curriculum-Grounded Benchmark for Evaluating LLMs as Mathematical Research Assistants in Graph Theorycited by 1
  5. 2026Cross-Model Cross-Language AI Coding Agent Performance: Accuracy and Speed of Parallel CLRS Algorithmscited by 0