As cloud service systems grow in scale and complexity, incidents that indicate unplanned interruptions and outages become unavoidable. Rapid and accurate triage of these incidents to the appropriate responsible teams is crucial to maintain service reliability and prevent significant financial losses. However, existing incident triage methods relying on manual operations and predefined rules often struggle with efficiency and accuracy due to the heterogeneity of incident data and the dynamic nature of domain knowledge across multiple teams.To solve these issues, we propose Triangle, an end-to-end incident triage system based on a Multi-Agent framework. Triangle leverages a semantic distillation mechanism to tackle the issue of semantic heterogeneity in incident data, enhancing the accuracy of incident triage. Additionally, we introduce multi-role agents and a negotiation mechanism to emulate human engineers’ workflows, effectively handling decentralized and dynamic domain knowledge from multiple teams. Furthermore, our system incorporates an automated troubleshooting information collection and mitigation mechanism, reducing the reliance on human labor and enabling fully automated end-to-end incident triage. Extensive experiments conducted on a real-world cloud production environment demonstrate that Triangle significantly improved incident triage accuracy (up to 97%) and reduced Time to Engage (TTE) by as much as 91%, demonstrating substantial operational impact across diverse cloud services.

Graph Database Management Systems (GDBMS), which utilize graph models for data storage and execute queries via graph traversals, have seen ubiquitous usage in real-world scenarios such as recommendation systems, knowledge graphs, and social networks. Much like Relational Database Management Systems (RDBMS), GDBMS are not immune to bugs. These bugs typically manifest as logic errors that yield incorrect results (e.g., omitting a node that should be included), performance bugs (e.g., long execution time caused by redundant graph scanning), and exception issues (e.g., unexpected or missing exceptions).

The advent of microservice architecture has led to a significant shift in the development of service-oriented software. In particular, the use of Remote Procedure Call (RPC), a mode of Inter-Process Communication (IPC) prevalent in microservices, has noticeably increased. To figure out the relationships between services and obtain a high-level understanding of service-oriented software, a line of recent work focuses on the dynamic construction of service call graphs, which relies on the preliminary deployment of services and only captures the calling relationships within a specific time frame. Meanwhile, static methods avoid the need for pre-deployment and often provide a more stable and complete graph compared to dynamic techniques. However, research and practical applications of static call graph construction remain relatively unexplored. This paper introduces RPCover, a novel gRPC dependency recovery framework that facilitates the interconnection of services across various programming languages using their static gRPC calls. In addition, due to the lack of a multilingual microservice benchmark that uses gRPC, we build the first multilingual benchmark RPCoverBench that contains complex gRPC call relations. RPCover has been evaluated on a single language benchmark (DeathStarBench) and our multilingual benchmark (RPCoverBench). The results show that RPCover effectively recovers 99.33% of the use cases of gRPC calls with less than 200% of the overhead compared with a single-language semantic dependency analyzer.