Silent Bug

Silent bugs, subtle errors in software or machine learning models that don't cause crashes but produce incorrect results, are a growing concern. Research focuses on automated detection methods, leveraging large language models (LLMs) to generate test cases that reveal these elusive bugs and on improving the robustness of machine learning models against concept and data drift, which can lead to silent failures. Understanding and mitigating silent bugs is crucial for ensuring the reliability and trustworthiness of software and AI systems across various applications, particularly in safety-critical domains.