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%.
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Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.
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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%.
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Towards Build Optimization Using Digital Twins
Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.