Introduces γ-CounterBoost policy that minimizes response time tail using only job types (no arrival times) in light-tailed M/G/1 queues for d≥2 types, extending Nudge-M.
The Gittins policy in the M/G/1 queue,
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SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
Survey and forum analysis of 683 Android developers finds they manually classify app data for Google's Data Safety Section or skip it, feel confident spotting collected data but not in translating it to the form, and worry about rejection.
PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.
MNAL reduces human effort in bug report labeling by up to 95.8% for readability and 196% for identifiability while improving identification performance and working with various neural models.
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Fine-Tuning Models for Automated Code Review Feedback
PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.