{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IFOH3GRQO37DIHVP6LXIB6VIKG","short_pith_number":"pith:IFOH3GRQ","schema_version":"1.0","canonical_sha256":"415c7d9a3076fe341eaff2ee80faa851aa59ada7dc9c456d5f5f9e59de269000","source":{"kind":"arxiv","id":"2402.17531","version":2},"attestation_state":"computed","paper":{"title":"Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Dongmei Zhang, Fangkai Yang, Hao Huang, Hua Ding, Junting Lu, Kaikai An, Liqun Li, Lu Wang, Pu Zhao, Qingwei Lin, Qi Zhang, Saravan Rajmohan, Yu Kang, Zhixing Ren","submitted_at":"2024-02-27T14:14:23Z","abstract_excerpt":"Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-h"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2402.17531","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-27T14:14:23Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"475d49e2b713e240ade76698e77e527896e1e59bb9b5579590bc490189f6e15d","abstract_canon_sha256":"0afe4a827c7b584de048c404236551709aeca854084608364811aa45ea62cc09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:31.374257Z","signature_b64":"LxtEKS6nEAj8TwmXaDC5eECz4PXVO+DnIGsM5/zjZShqQyWP3ONMBY9Ij9/655kO3bXKnNSzjvzDU0DbpH1IBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"415c7d9a3076fe341eaff2ee80faa851aa59ada7dc9c456d5f5f9e59de269000","last_reissued_at":"2026-07-05T08:17:31.373762Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:31.373762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Dongmei Zhang, Fangkai Yang, Hao Huang, Hua Ding, Junting Lu, Kaikai An, Liqun Li, Lu Wang, Pu Zhao, Qingwei Lin, Qi Zhang, Saravan Rajmohan, Yu Kang, Zhixing Ren","submitted_at":"2024-02-27T14:14:23Z","abstract_excerpt":"Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.17531","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2402.17531/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2402.17531","created_at":"2026-07-05T08:17:31.373821+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.17531v2","created_at":"2026-07-05T08:17:31.373821+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.17531","created_at":"2026-07-05T08:17:31.373821+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFOH3GRQO37D","created_at":"2026-07-05T08:17:31.373821+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFOH3GRQO37DIHVP","created_at":"2026-07-05T08:17:31.373821+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFOH3GRQ","created_at":"2026-07-05T08:17:31.373821+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30479","citing_title":"COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2506.01481","citing_title":"TSGuard: Automated User-Centric Incident Diagnosis for AI Workloads in the Cloud","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2510.10074","citing_title":"StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosis","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02906","citing_title":"OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02906","citing_title":"OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG","json":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG.json","graph_json":"https://pith.science/api/pith-number/IFOH3GRQO37DIHVP6LXIB6VIKG/graph.json","events_json":"https://pith.science/api/pith-number/IFOH3GRQO37DIHVP6LXIB6VIKG/events.json","paper":"https://pith.science/paper/IFOH3GRQ"},"agent_actions":{"view_html":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG","download_json":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG.json","view_paper":"https://pith.science/paper/IFOH3GRQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.17531&json=true","fetch_graph":"https://pith.science/api/pith-number/IFOH3GRQO37DIHVP6LXIB6VIKG/graph.json","fetch_events":"https://pith.science/api/pith-number/IFOH3GRQO37DIHVP6LXIB6VIKG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG/action/storage_attestation","attest_author":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG/action/author_attestation","sign_citation":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG/action/citation_signature","submit_replication":"https://pith.science/pith/IFOH3GRQO37DIHVP6LXIB6VIKG/action/replication_record"}},"created_at":"2026-07-05T08:17:31.373821+00:00","updated_at":"2026-07-05T08:17:31.373821+00:00"}