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arXiv · 2503.12185

FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents

Sándor Battaglini-Fischer·Nishanthi Srinivasan·Bálint László Szarvas·Xiaoyu Chu·Alexandru Iosup
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Abstract

Large Language Model (LLM) services such as ChatGPT, DALLE, and Cursor have quickly become essential for society, businesses, and individuals, empowering applications such as chatbots, image generation, and code assistance. The complexity of LLM systems makes them prone to failures and affects their reliability and availability, yet their failure patterns are not fully understood, making it an emerging problem. However, there are limited datasets and studies in this area, particularly lacking an open-access tool for analyzing LLM service failures based on incident reports. Addressing these problems, in this work we propose FAILS, the first open-sourced framework for incident reports collection and analysis on different LLM services and providers. FAILS provides comprehensive data collection, analysis, and visualization capabilities, including:(1) It can automatically collect, clean, and update incident data through its data scraper and processing components;(2) It provides 17 types of failure analysis, allowing users to explore temporal trends of incidents, analyze service reliability metrics, such as Mean Time to Recovery (MTTR) and Mean Time Between Failures (MTBF);(3) It leverages advanced LLM tools to assist in data analysis and interpretation, enabling users to gain observations and insights efficiently. All functions are integrated in the backend, allowing users to easily access them through a web-based frontend interface. FAILS supports researchers, engineers, and general users to understand failure patterns and further mitigate operational incidents and outages in LLM services. The framework is publicly available on https://github.com/atlarge-research/FAILS.

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Sándor Battaglini-Fischer, Nishanthi Srinivasan, Bálint László Szarvas, Xiaoyu Chu, Alexandru Iosup. 2025-03-15. FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents. https://doi.org/10.1145/3680256.3721320

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