Large language models (LLMs) are increasingly used for code generation, making reliable identification of machine-generated code important for attribution, tracking, and misuse detection. Existing code watermarking methods are dominated by N-gram-based schemes, yet their robustness has mostly been evaluated only against simple edits or optimizations. We argue that this significantly overstates security, because software engineering already provides stronger semantics-preserving transformations in the form of code obfuscation.

We study N-gram-based code watermarking under obfuscation. We formally model semantics-preserving transformations as a Markov random walk and prove that, under an intuitive and experimentally supported assumption called distribution consistency, obfuscation can nullify the robustness of N-gram-based watermarks. If the original detector has a false positive rate 𝜖_pos, then after obfuscation, its failure rate on watermarked code approaches 1 - 𝜖_pos.

We validate this theory on three state-of-the-art watermarking schemes, two LLMs, two programming languages, four benchmarks, and four obfuscators. Across all settings, detectors collapse to near-random performance on obfuscated code (AUROC tightly around 0.5), and for each language, at least one attack leaves all post-obfuscation AUROC scores below 0.6. These results jointly show that current N-gram-based code watermarks are not robust to realistic obfuscation attacks and motivate more semantics-aware alternatives.

Formal postconditions precisely characterize program behavior and support debugging, testing, and verification, but writing them requires substantial expertise and effort. This has motivated recent work on automatically generating postconditions from code and natural-language artifacts using large language models (LLMs). However, evaluation remains a key bottleneck. Existing benchmarks primarily emphasize correctness under limited evaluation settings, often relying on surface-form matching or manual assessment on small or synthetic datasets.We introduce POSTCONDBENCH, a multilingual benchmark for evaluating method-level postcondition generation from real-world software. POSTCONDBENCH comprises 420 Python and Java tasks drawn from 121 open-source projects, each paired with a high-quality ground-truth postcondition set constructed with expert involvement. To enable automatic evaluation, POSTCONDBENCH provides a runnable execution environment and operationalizes completeness via defect discrimination: a postcondition set is more complete if it is violated by more defective implementations while remaining satisfied on correct executions. Using POSTCONDBENCH, we formulate three generation settings and evaluate five SOTA LLMs. Our results reveal a substantial gap between correctness and completeness, and show that repository-level dependencies and method complexity exacerbate this gap.