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%.
Ghaleb, and Lionel Briand
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Unsupervised domain adaptation with GSDE achieves ~80% accuracy in cross-process TIG-laser weld penetration prediction, improving supervised baselines by over 43%.
citing papers explorer
-
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%.
-
A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
Unsupervised domain adaptation with GSDE achieves ~80% accuracy in cross-process TIG-laser weld penetration prediction, improving supervised baselines by over 43%.