LibEvoBench benchmark shows LLMs are version-oblivious on evolving APIs, with documentation helping but version specification not.
LLM Hallucinations in Practical Code Generation : Phenomena , Mechanism , and Mitigation
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A study of seven LLMs finds that realistic prompt variations such as one-character misspellings trigger library hallucinations in up to 26% of cases, fabricated names in up to 99%, and time-based prompts in up to 85%, and introduces LibHalluBench for evaluation.
EyeMulator augments CodeLLM fine-tuning loss with token weights derived from human eye-tracking scan paths, producing large gains on code translation and summarization across StarCoder, Llama-3.2 and DeepSeek-Coder.
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
Proposes guidance for responsible AI use in scientific software development under NQA-1 standards, illustrated with TMAP8 V&V cases to ensure accountability and auditability.
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Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability
Proposes guidance for responsible AI use in scientific software development under NQA-1 standards, illustrated with TMAP8 V&V cases to ensure accountability and auditability.