CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs
Dung Nguyen Manh, Thang Phan Chau, Nam Le Hai, Thong T. Doan, Nam V. Nguyen, Quang Pham, Nghi D. Q. Bui
2024-10-07
Summary
This paper introduces CodeMMLU, a new benchmark designed to evaluate how well Code Large Language Models (CodeLLMs) understand and analyze code, rather than just generating it.
What's the problem?
While recent advancements in CodeLLMs have focused mainly on generating code, there has been a lack of emphasis on understanding code. This is a problem because simply generating code without understanding can lead to errors and inefficiencies in software development. There needs to be a way to assess how well these models comprehend code and related concepts.
What's the solution?
To address this issue, the authors created CodeMMLU, which consists of over 10,000 multiple-choice questions that test various aspects of code understanding across different programming languages. The questions cover topics like code analysis, defect detection, and software engineering principles. Unlike traditional tests that focus on generation, CodeMMLU assesses the models' reasoning abilities about code, providing insights into their comprehension of complex software concepts.
Why it matters?
This research is important because it highlights the need for better evaluation methods for AI models used in coding tasks. By focusing on understanding rather than just generation, CodeMMLU aims to improve the reliability and effectiveness of AI-assisted software development tools. This can lead to better coding assistants that help developers write more accurate and efficient code.
Abstract
Recent advancements in Code Large Language Models (CodeLLMs) have predominantly focused on open-ended code generation tasks, often neglecting the critical aspect of code understanding and comprehension. To bridge this gap, we present CodeMMLU, a comprehensive multiple-choice question-answer benchmark designed to evaluate the depth of software and code understanding in LLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles across multiple programming languages. Unlike traditional benchmarks, CodeMMLU assesses models's ability to reason about code rather than merely generate it, providing deeper insights into their grasp of complex software concepts and systems. Our extensive evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU, highlighting deficiencies in comprehension beyond code generation. By underscoring the crucial relationship between code understanding and effective generation, CodeMMLU serves as a vital resource for advancing AI-assisted software development, ultimately aiming to create more reliable and capable coding assistants.