From Syntactic Mimicry to Semantic Correctness: Behavioral Understanding in LLM Code Generation
TL;DR
This work addresses three interconnected challenges in building behaviorally-grounded code generation: retrieving not just what symbols are, but how they behave through usage patterns; adapting practices across contexts by separating behavioral intent from implementation details; and enforcing semantic properties during generation without prohibitive runtime costs.
Abstract
Large Language Models (LLMs) have transformed software engineering by automating code generation, yet they frequently produce code that is syntactically correct but behaviorally incorrect—failing to respect repository conventions, misapplying practices across contexts, or violating semantic properties. These failures come from a fundamental limitation: current LLMs learn syntactic patterns but lack behavioral understanding of how code executes in context. This work addresses three interconnected challenges in building behaviorally-grounded code generation: (1) retrieving not just what symbols are, but how they behave through usage patterns; (2) adapting practices across contexts by separating behavioral intent from implementation details; and (3) enforcing semantic properties during generation without prohibitive runtime costs. Preliminary results demonstrate substantial improvements: 48.2% exact match on repository completion (vs. 29.05% baseline) and 100% conformance on cross-framework practice transfer (vs. 0% baseline). This work aims to transform LLMs from pattern-matching systems into behaviorally-aware generation system that understand what code does, not just what it looks like.