MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
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AI agents modify logging less often than humans in 58.4% of repositories but produce higher log density when they change it; explicit logging instructions are rare (4.7%) and ignored 67% of the time, with humans performing 72.5% of post-generation log repairs.
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Single-Language Evidence Is Insufficient for Automated Logging: A Multilingual Benchmark and Empirical Study with LLMs
MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
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Do AI Coding Agents Log Like Humans? An Empirical Study
AI agents modify logging less often than humans in 58.4% of repositories but produce higher log density when they change it; explicit logging instructions are rare (4.7%) and ignored 67% of the time, with humans performing 72.5% of post-generation log repairs.