Code LLMs generate substantially worse comments outside English, and no tested automatic metric or LLM judge reliably matches human assessment of those outputs.
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning , url=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
POSTCONDBENCH is a new multilingual benchmark that evaluates LLM postcondition generation on real code using defect discrimination to assess completeness beyond surface matching.
SPECA derives categorized security properties from specifications to enable cross-implementation auditing of distributed protocols, recovering all 15 expert-augmented vulnerabilities on an Ethereum contest and achieving 88.9% precision at 100% recall on a C/C++ benchmark.
citing papers explorer
-
Evaluating Non-English Developer Support in Machine Learning for Software Engineering
Code LLMs generate substantially worse comments outside English, and no tested automatic metric or LLM judge reliably matches human assessment of those outputs.
-
POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference
POSTCONDBENCH is a new multilingual benchmark that evaluates LLM postcondition generation on real code using defect discrimination to assess completeness beyond surface matching.
-
Beyond Code Reasoning: Specification-Anchored Auditing of Multi-Implementation Distributed Protocols
SPECA derives categorized security properties from specifications to enable cross-implementation auditing of distributed protocols, recovering all 15 expert-augmented vulnerabilities on an Ethereum contest and achieving 88.9% precision at 100% recall on a C/C++ benchmark.