Measuring LLM Code Generation Stability via Structural Entropy

• URL: http://arxiv.org/abs/2508.14288v1
• Date: Tue, 19 Aug 2025 22:07:12 GMT
• Title: Measuring LLM Code Generation Stability via Structural Entropy
• Authors: Yewei Song, Tiezhu Sun, Xunzhu Tang, Prateek Rajput, Tegawende F. Bissyande, Jacques Klein,
• Abstract summary: We extend "structural-entropy concepts" to the program domain by pairing entropy with abstract syntax tree (AST) analysis.<n>We measure stability in two complementary ways: (i) Jensen-Shannon divergence, a symmetric, bounded indicator of structural overlap, and (ii) a Structural Cross-Entropy ratio that highlights missing high-probability patterns.<n>Unlike pass@k, BLEU, or CodeBLEU, our metrics are reference-free, language-agnostic, and execution-independent.
• Score: 4.812266013066678
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Assessing the stability of code generation from large language models (LLMs) is essential for judging their reliability in real-world development. We extend prior "structural-entropy concepts" to the program domain by pairing entropy with abstract syntax tree (AST) analysis. For any fixed prompt, we collect the multiset of depth-bounded subtrees of AST in each generated program and treat their relative frequencies as a probability distribution. We then measure stability in two complementary ways: (i) Jensen-Shannon divergence, a symmetric, bounded indicator of structural overlap, and (ii) a Structural Cross-Entropy ratio that highlights missing high-probability patterns. Both metrics admit structural-only and token-aware variants, enabling separate views on control-flow shape and identifier-level variability. Unlike pass@k, BLEU, or CodeBLEU, our metrics are reference-free, language-agnostic, and execution-independent. We benchmark several leading LLMs on standard code generation tasks, demonstrating that AST-driven structural entropy reveals nuances in model consistency and robustness. The method runs in O(n,d) time with no external tests, providing a lightweight addition to the code-generation evaluation toolkit.

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