# Evaluating Code Generation of LLMs in Advanced Computer Science Problems

arXiv: https://arxiv.org/abs/2504.14964 · HTML: https://mlsanity.com/paper/2504.14964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Analyzing LLM Usage in an Advanced Computing Class in India](https://mlsanity.com/paper/2404.04603/) (2024)
- ["ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses](https://mlsanity.com/paper/2404.17443/) (2024)
- [Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses](https://mlsanity.com/paper/2410.06865/) (2024)
- [Insights from the Frontline: GenAI Utilization Among Software Engineering Students](https://mlsanity.com/paper/2412.15624/) (2024)

## Abstract (arXiv)

Large Language Models (LLMs), such as GitHub Copilot and ChatGPT have become popular among programming students. Students use LLMs to assist them in programming courses, including generating source code. Previous work has evaluated the ability of LLMs in solving introductory-course programming assignments. The results have shown that LLMs are highly effective in generating code for introductory Computer Science (CS) courses. However, there is a gap in research on evaluating LLMs' ability to generate code that solves advanced programming assignments. In this work, we evaluate the ability of four LLM tools to solve programming assignments from advanced CS courses in three popular programming languages, Java, Python, and C. We manually select 12 problems, three problems from introductory courses as the baseline and nine programming assignments from second- and third-year CS courses. To evaluate the LLM-generated code, we generate a test suite of 1000 test cases per problem and analyze the program output. Our evaluation shows that although LLMs are highly effective in generating source code for introductory programming courses, solving advanced programming assignments is more challenging. Nonetheless, in many cases, LLMs identify the base problem and provide partial solutions that may be useful to CS students. Furthermore, our results may provide useful guidance for teachers of advanced programming courses on how to design programming assignments.