PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911629770227712 |
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| author | Cui, Shengkun Krishna, Rahul Jha, Saurabh Iyer, Ravishankar K. |
| author_facet | Cui, Shengkun Krishna, Rahul Jha, Saurabh Iyer, Ravishankar K. |
| contents | Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and deploys an agentic workflow for diagnosing code- and configuration-caused cloud incidents. PRAXIS employs an LLM-driven structured traversal over two types of graph: (1) a service dependency graph (SDG) that captures microservice-level dependencies; and (2) a hammock-block program dependence graph (PDG) that captures code-level dependencies for each microservice. Compared to state-of-the-art ReAct baselines, PRAXIS improves RCA accuracy by up to 6.3x while reducing token consumption by 5.3x. PRAXIS is demonstrated on a set of 30 comprehensive real-world incidents that is being compiled into an RCA benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis Cui, Shengkun Krishna, Rahul Jha, Saurabh Iyer, Ravishankar K. Distributed, Parallel, and Cluster Computing Artificial Intelligence Software Engineering Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and deploys an agentic workflow for diagnosing code- and configuration-caused cloud incidents. PRAXIS employs an LLM-driven structured traversal over two types of graph: (1) a service dependency graph (SDG) that captures microservice-level dependencies; and (2) a hammock-block program dependence graph (PDG) that captures code-level dependencies for each microservice. Compared to state-of-the-art ReAct baselines, PRAXIS improves RCA accuracy by up to 6.3x while reducing token consumption by 5.3x. PRAXIS is demonstrated on a set of 30 comprehensive real-world incidents that is being compiled into an RCA benchmark. |
| title | PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2512.22113 |