Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models

TL;DR

This study compares the effectiveness of GPT-3, GPT-4, and novice students in detecting logic errors in code, highlighting the advancements of LLMs and their potential educational applications.

Contribution

It provides a comparative analysis of LLMs and students in logic error detection, demonstrating LLMs' superior performance and exploring their integration into educational tools.

Findings

GPT-4 outperforms GPT-3 and students in logic error detection

Both GPT-3 and GPT-4 significantly outperform students

LLMs show promise for supporting novice programming education

Abstract

Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle. In certain conditions, buggy code may even exhibit correct behavior -- in other cases, the issue might be about how a problem statement has been interpreted. Such errors can be hard to spot when reading the code, and they can also at times be missed by automated tests. There is great educational potential in automatically detecting logic errors, especially when paired with suitable feedback for novices. Large language models (LLMs) have recently demonstrated surprising performance for a range of computing tasks, including generating and explaining code. These capabilities are closely linked to code syntax, which aligns with the next token prediction behavior of LLMs. On the other hand, logic errors relate to the runtime performance of code and thus may not be as well suited to analysis by LLMs. To explore this, we investigate the performance of two popular LLMs, GPT-3 and GPT-4, for detecting and providing a novice-friendly explanation of logic errors. We compare LLM performance with a large cohort of introductory computing students $(n=964)$ solving the same error detection task. Through a mixed-methods analysis of student and model responses, we observe significant improvement in logic error identification between the previous and current generation of LLMs, and find that both LLM generations significantly outperform students. We outline how such models could be integrated into computing education tools, and discuss their potential for supporting students when learning programming.