  Incident management for large cloud services is a complex and tedious process
and requires significant amount of manual efforts from on-call engineers
(OCEs). OCEs typically leverage data from different stages of the software
development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service
properties, service dependencies, trouble-shooting documents, etc.) to generate
insights for detection, root causing and mitigating of incidents. Recent
advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini)
created opportunities to automatically generate contextual recommendations to
the OCEs assisting them to quickly identify and mitigate critical issues.
However, existing research typically takes a silo-ed view for solving a certain
task in incident management by leveraging data from a single stage of SDLC. In
this paper, we demonstrate that augmenting additional contextual data from
different stages of SDLC improves the performance of two critically important
and practically challenging tasks: (1) automatically generating root cause
recommendations for dependency failure related incidents, and (2) identifying
ontology of service monitors used for automatically detecting incidents. By
leveraging 353 incident and 260 monitor dataset from Microsoft, we demonstrate
that augmenting contextual information from different stages of the SDLC
improves the performance over State-of-The-Art methods.
