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MATCH: Task-Driven Code Evaluation through Contrastive Learning

arXiv — cs.CL•Wednesday, October 29, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new study highlights the challenges of evaluating AI-generated code, particularly in how well it meets developer intent. With tools like GitHub Copilot generating a significant portion of code, traditional evaluation methods are proving inadequate. This research introduces a novel approach using contrastive learning to improve code evaluation, which could lead to more effective and scalable solutions in the future. This matters because as AI continues to play a larger role in software development, ensuring the quality and functionality of generated code is crucial for developers and the industry as a whole.
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