{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ISI5RKBWFX6DPOR3MHO22JRVXG","short_pith_number":"pith:ISI5RKBW","schema_version":"1.0","canonical_sha256":"4491d8a8362dfc37ba3b61ddad2635b999c9f05c4966e76324fe575e32f9c52c","source":{"kind":"arxiv","id":"2501.16998","version":1},"attestation_state":"computed","paper":{"title":"Large Language Models for Code Generation: The Practitioners Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aakash Ahmad, Jussi Rasku, Kai Kristian Kemell, Kari Syst\\\"a, Malik Abdul Sami, Muhammad Waseem, Pekka Abrahamsson, Zeeshan Rasheed","submitted_at":"2025-01-28T14:52:16Z","abstract_excerpt":"Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are developing various tools, benchmarks, and metrics to evaluate the effectiveness of LLM-generated code. However, there is a lack of solutions evaluated through empirically grounded methods that incorporate practitioners perspectives to assess functionality, syntax, and accuracy in real world applications. To address this gap, we propose and develop a multi-m"},"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":"2501.16998","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-01-28T14:52:16Z","cross_cats_sorted":[],"title_canon_sha256":"528ac130a1facc9a9e876c224c7bbbb5187ae922f9e58fffc32330e7df3e569a","abstract_canon_sha256":"5878b3fa6138f89bcb222d331146de05afb0a9b853805f8ad9a530f19982959e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:21.481906Z","signature_b64":"MhI8o5v1HZU1AAArf3NiIdwdgw3YWU7QmFK4LM8aapPNIOl801+Xqr2MkZZr5zT7J7ancZDZT9YHpwgi9NsMCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4491d8a8362dfc37ba3b61ddad2635b999c9f05c4966e76324fe575e32f9c52c","last_reissued_at":"2026-07-05T10:06:21.481488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:21.481488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models for Code Generation: The Practitioners Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aakash Ahmad, Jussi Rasku, Kai Kristian Kemell, Kari Syst\\\"a, Malik Abdul Sami, Muhammad Waseem, Pekka Abrahamsson, Zeeshan Rasheed","submitted_at":"2025-01-28T14:52:16Z","abstract_excerpt":"Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are developing various tools, benchmarks, and metrics to evaluate the effectiveness of LLM-generated code. However, there is a lack of solutions evaluated through empirically grounded methods that incorporate practitioners perspectives to assess functionality, syntax, and accuracy in real world applications. To address this gap, we propose and develop a multi-m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16998","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/2501.16998/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":"2501.16998","created_at":"2026-07-05T10:06:21.481545+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.16998v1","created_at":"2026-07-05T10:06:21.481545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16998","created_at":"2026-07-05T10:06:21.481545+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISI5RKBWFX6D","created_at":"2026-07-05T10:06:21.481545+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISI5RKBWFX6DPOR3","created_at":"2026-07-05T10:06:21.481545+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISI5RKBW","created_at":"2026-07-05T10:06:21.481545+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.23986","citing_title":"TusoAI: Agentic Optimization for Scientific Methods","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG","json":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG.json","graph_json":"https://pith.science/api/pith-number/ISI5RKBWFX6DPOR3MHO22JRVXG/graph.json","events_json":"https://pith.science/api/pith-number/ISI5RKBWFX6DPOR3MHO22JRVXG/events.json","paper":"https://pith.science/paper/ISI5RKBW"},"agent_actions":{"view_html":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG","download_json":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG.json","view_paper":"https://pith.science/paper/ISI5RKBW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.16998&json=true","fetch_graph":"https://pith.science/api/pith-number/ISI5RKBWFX6DPOR3MHO22JRVXG/graph.json","fetch_events":"https://pith.science/api/pith-number/ISI5RKBWFX6DPOR3MHO22JRVXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG/action/storage_attestation","attest_author":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG/action/author_attestation","sign_citation":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG/action/citation_signature","submit_replication":"https://pith.science/pith/ISI5RKBWFX6DPOR3MHO22JRVXG/action/replication_record"}},"created_at":"2026-07-05T10:06:21.481545+00:00","updated_at":"2026-07-05T10:06:21.481545+00:00"}