Researchers from the University of Wisconsin–Madison and Amazon Web Services studied improving Large Language Models of code (Code-LLMs) to detect potential bugs. They introduced the task of buggy-code completion (bCC), evaluated on datasets buggy-HumanEval and buggy-FixEval. Code-LLMs’ performance degraded significantly, for which post-mitigation methods were proposed, although performance gaps persisted. The work enhances understanding of Code-LLMs in bCC and suggests ways to improve code completion with potential bugs.
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AI Research: Improving Code Generation and Bug Detection
Programming can be complex and error-prone. Large language models of code (Code-LLMs) have been developed to help with code completion, but they can sometimes overlook bugs in the code context. Researchers from the University of Wisconsin–Madison and Amazon Web Services have conducted a study to improve the performance of LLMs in detecting potential bugs during code generation.
Key Findings:
- The research introduces the concept of buggy-code completion (bCC) to address potential bugs during code generation.
- Code-LLMs’ performance degrades significantly in the presence of bugs, with test-case pass rates dropping below 5%.
- Proposed mitigation methods include Removal-then-completion, Completion-then-rewriting, and Rewriting-then-completion to address the issue.
- The study is evaluated on two datasets named buggy-HumanEval and buggy-FixEval.
Practical AI Solutions:
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