Longitudinal analysis of over 4000 toggle events in Kubernetes and GitLab shows removals lag additions, leading to growing inventories with median lifespans of 734 and 185 days, plus a benchmarking framework with five metrics.
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FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
citing papers explorer
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Feature Toggle Dynamics in Large-Scale Systems: Prevalence, Growth, Lifespan, and Benchmarking
Longitudinal analysis of over 4000 toggle events in Kubernetes and GitLab shows removals lag additions, leading to growing inventories with median lifespans of 734 and 185 days, plus a benchmarking framework with five metrics.
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Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.