An AST pattern-matching prototype with a custom DSL achieves 0.74 average F1-score on a BigCloneEval subset, outperforming CodeLlama (0.35) and code clone detectors (best recall 0.20).
Available: https://arxiv.org/abs/2006.15682
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Hybrid LLM plus static analysis for algorithm recognition in code cuts required model calls by 72-97% and lifts F1-scores by as much as 12 points.
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Exploring the Effectiveness of Abstract Syntax Tree Patterns for Algorithm Recognition
An AST pattern-matching prototype with a custom DSL achieves 0.74 average F1-score on a BigCloneEval subset, outperforming CodeLlama (0.35) and code clone detectors (best recall 0.20).
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Combining Static Code Analysis and Large Language Models Improves Correctness and Performance of Algorithm Recognition
Hybrid LLM plus static analysis for algorithm recognition in code cuts required model calls by 72-97% and lifts F1-scores by as much as 12 points.