{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FAU2LSXSD5SQ63FJJJ7CDJVDRY","short_pith_number":"pith:FAU2LSXS","schema_version":"1.0","canonical_sha256":"2829a5caf21f650f6ca94a7e21a6a38e1596325e161c8c27275e0dfef5775a51","source":{"kind":"arxiv","id":"2312.11719","version":1},"attestation_state":"computed","paper":{"title":"How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.SE","authors_text":"Anita Sarma, Dylan Liu, Igor Steinmacher, Marco Gerosa, Rudrajit Choudhuri","submitted_at":"2023-12-18T21:38:00Z","abstract_excerpt":"Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resour"},"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":"2312.11719","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-12-18T21:38:00Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"1008af5c3bbde2af080c936c9eac25491f63ed64cc86297bbc4d108fa5161073","abstract_canon_sha256":"0e063e793d044f843d9552e072aac2371f20ee053844a3543ca6043e4b068cba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:14.784511Z","signature_b64":"gzhamfBymRxRIglqnRmmoPgixex9CUPSwcCde8hbSe9ire57CeMuSUCAvegXCJJlmyL5CJh/6g2I2/QBtiBqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2829a5caf21f650f6ca94a7e21a6a38e1596325e161c8c27275e0dfef5775a51","last_reissued_at":"2026-07-05T07:52:14.784025Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:14.784025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.SE","authors_text":"Anita Sarma, Dylan Liu, Igor Steinmacher, Marco Gerosa, Rudrajit Choudhuri","submitted_at":"2023-12-18T21:38:00Z","abstract_excerpt":"Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resour"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11719","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/2312.11719/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":"2312.11719","created_at":"2026-07-05T07:52:14.784085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.11719v1","created_at":"2026-07-05T07:52:14.784085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11719","created_at":"2026-07-05T07:52:14.784085+00:00"},{"alias_kind":"pith_short_12","alias_value":"FAU2LSXSD5SQ","created_at":"2026-07-05T07:52:14.784085+00:00"},{"alias_kind":"pith_short_16","alias_value":"FAU2LSXSD5SQ63FJ","created_at":"2026-07-05T07:52:14.784085+00:00"},{"alias_kind":"pith_short_8","alias_value":"FAU2LSXS","created_at":"2026-07-05T07:52:14.784085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2411.09916","citing_title":"\"Should I Give Up Now?\" Investigating LLM Pitfalls in Software Engineering","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY","json":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY.json","graph_json":"https://pith.science/api/pith-number/FAU2LSXSD5SQ63FJJJ7CDJVDRY/graph.json","events_json":"https://pith.science/api/pith-number/FAU2LSXSD5SQ63FJJJ7CDJVDRY/events.json","paper":"https://pith.science/paper/FAU2LSXS"},"agent_actions":{"view_html":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY","download_json":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY.json","view_paper":"https://pith.science/paper/FAU2LSXS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.11719&json=true","fetch_graph":"https://pith.science/api/pith-number/FAU2LSXSD5SQ63FJJJ7CDJVDRY/graph.json","fetch_events":"https://pith.science/api/pith-number/FAU2LSXSD5SQ63FJJJ7CDJVDRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY/action/storage_attestation","attest_author":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY/action/author_attestation","sign_citation":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY/action/citation_signature","submit_replication":"https://pith.science/pith/FAU2LSXSD5SQ63FJJJ7CDJVDRY/action/replication_record"}},"created_at":"2026-07-05T07:52:14.784085+00:00","updated_at":"2026-07-05T07:52:14.784085+00:00"}