[2601.22264] Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models

Computer Science > Software Engineering arXiv:2601.22264 (cs) [Submitted on 29 Jan 2026 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models Authors:Henri Aïdasso, Francis Bordeleau, Ali Tizghadam View a PDF of the paper titled Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models, by Henri A\"idasso and 2 other authors View PDF HTML (experimental) Abstract:In principle, Continuous Integration (CI) pipeline failures provide valuable feedback to developers on code-related errors. In practice, however, pipeline jobs often fail intermittently due to non-deterministic tests, network outages, infrastructure failures, resource exhaustion, and other reliability issues. These intermittent (flaky) job failures lead to substantial inefficiencies: wasted computational resources from repeated reruns and significant diagnosis time that distracts developers from core activities and often requires intervention from specialized teams. Prior work has proposed machine learning techniques to detect intermittent failures, but does not address the subsequent diagnosis challenge. To fill this gap, we introduce FlaXifyer, a few-shot learning approach for predicting intermittent job failure categories using pre-trained language models. FlaXifyer requires only job execution logs and achieves 84.3% Macro F1 and 92.0% Top-2 accuracy with just 12 l...