Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models - Ahmed, Toufique; GHOSH, Supriyo; Bansal, Chetan; Zimmermann, Tom; Zhang, Xuchao; Rajmohan, Saravan (ICSE 2023, 2023)
Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. Oncall engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence have resulted in state-of-the-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evaluate the effectiveness of these models for helping engineers root cause and mitigate production incidents. We do a rigorous study on more than 40,000 incidents and compare several large language models in zero-shot, fine-tuned and multi-task setting using semantic and lexical metrics. Lastly, our human evaluation with actual incident owners shows the efficacy and future potential of using artificial intelligence for resolving cloud incidents.