Summary of Bug in the Code Stack: Can Llms Find Bugs in Large Python Code Stacks, by Hokyung Lee et al.
Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks
by Hokyung Lee, Sumanyu Sharma, Bing Hu
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the capabilities of Large Language Models (LLMs) in code-based environments, specifically in retrieving contextual information from large source code. The authors design a benchmark, Bug In The Code Stack (BICS), to evaluate the ability of LLMs to identify simple syntax bugs within large source code. The findings reveal that code-based environments pose a significant challenge compared to text-based environments for retrieval tasks, and there is a substantial performance disparity among different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well Large Language Models (LLMs) can understand code and find mistakes in it. They make a special test called Bug In The Code Stack (BICS) to see if LLMs can spot simple coding errors. What they found out is that LLMs have a harder time with code than they do with text, and different LLMs are better at this task than others. |
Keywords
» Artificial intelligence » Syntax
