{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:57LA2JPE7J33BNZQC2KA3JBJUL","short_pith_number":"pith:57LA2JPE","schema_version":"1.0","canonical_sha256":"efd60d25e4fa77b0b73016940da429a2df1c1319941e009b41e74634ed811d1b","source":{"kind":"arxiv","id":"2303.07839","version":1},"attestation_state":"computed","paper":{"title":"ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Douglas C. Schmidt, Jesse Spencer-Smith, Jules White, Quchen Fu, Sam Hays","submitted_at":"2023-03-11T14:43:17Z","abstract_excerpt":"This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explor"},"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":"2303.07839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-11T14:43:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"312d6d7a465bbaf75b1d8831d928af164888f4911d8515058d66d78d5bd07ee9","abstract_canon_sha256":"4350a4ae77305066c7909997bb90d1e15fb9aaeec13677e18865629bbbb4908f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:12.116793Z","signature_b64":"UF4YkWbmFV6cR/NiUJVCUKkk+Uh3NJDIOt3Hnv5qBvDg9feSOR1ycxEUbM39O7M2UynBjZJZWv6CKxPV/6z0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efd60d25e4fa77b0b73016940da429a2df1c1319941e009b41e74634ed811d1b","last_reissued_at":"2026-07-05T05:51:12.116254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:12.116254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Douglas C. Schmidt, Jesse Spencer-Smith, Jules White, Quchen Fu, Sam Hays","submitted_at":"2023-03-11T14:43:17Z","abstract_excerpt":"This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07839","kind":"arxiv","version":1},"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/2303.07839/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":"2303.07839","created_at":"2026-07-05T05:51:12.116326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.07839v1","created_at":"2026-07-05T05:51:12.116326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07839","created_at":"2026-07-05T05:51:12.116326+00:00"},{"alias_kind":"pith_short_12","alias_value":"57LA2JPE7J33","created_at":"2026-07-05T05:51:12.116326+00:00"},{"alias_kind":"pith_short_16","alias_value":"57LA2JPE7J33BNZQ","created_at":"2026-07-05T05:51:12.116326+00:00"},{"alias_kind":"pith_short_8","alias_value":"57LA2JPE","created_at":"2026-07-05T05:51:12.116326+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02096","citing_title":"Foundation Models as Oracles for Refactoring Correctness Detection","ref_index":65,"is_internal_anchor":false},{"citing_arxiv_id":"2404.01535","citing_title":"Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.00851","citing_title":"Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26590","citing_title":"Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23124","citing_title":"ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01392","citing_title":"Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02096","citing_title":"Foundation Models as Oracles for Refactoring Correctness Detection","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL","json":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL.json","graph_json":"https://pith.science/api/pith-number/57LA2JPE7J33BNZQC2KA3JBJUL/graph.json","events_json":"https://pith.science/api/pith-number/57LA2JPE7J33BNZQC2KA3JBJUL/events.json","paper":"https://pith.science/paper/57LA2JPE"},"agent_actions":{"view_html":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL","download_json":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL.json","view_paper":"https://pith.science/paper/57LA2JPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.07839&json=true","fetch_graph":"https://pith.science/api/pith-number/57LA2JPE7J33BNZQC2KA3JBJUL/graph.json","fetch_events":"https://pith.science/api/pith-number/57LA2JPE7J33BNZQC2KA3JBJUL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL/action/storage_attestation","attest_author":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL/action/author_attestation","sign_citation":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL/action/citation_signature","submit_replication":"https://pith.science/pith/57LA2JPE7J33BNZQC2KA3JBJUL/action/replication_record"}},"created_at":"2026-07-05T05:51:12.116326+00:00","updated_at":"2026-07-05T05:51:12.116326+00:00"}