{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RGZ7JCOM5W5KADWP62FCOUCX2Q","short_pith_number":"pith:RGZ7JCOM","schema_version":"1.0","canonical_sha256":"89b3f489ccedbaa00ecff68a275057d4015142d16b53a06d0ed4fd7db66e9c40","source":{"kind":"arxiv","id":"2303.03482","version":1},"attestation_state":"computed","paper":{"title":"Recent Advances in Software Effort Estimation using Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Victor Uc-Cetina","submitted_at":"2023-03-06T20:25:16Z","abstract_excerpt":"An increasing number of software companies have already realized the importance of storing project-related data as valuable sources of information for training prediction models. Such kind of modeling opens the door for the implementation of tailored strategies to increase the accuracy in effort estimation of whole teams of engineers. In this article we review the most recent machine learning approaches used to estimate software development efforts for both, non-agile and agile methodologies. We analyze the benefits of adopting an agile methodology in terms of effort estimation possibilities, "},"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":"2303.03482","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-03-06T20:25:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c1e000e5902adbd008b263ea24772be0651a913b7c3ea48112c00e74c46e9a2c","abstract_canon_sha256":"e1449723d510998cb2a8367962e417a6d0d3ceb100b70e085378cbfc31053fc5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:55.098917Z","signature_b64":"h3Nft9qYS3LQDLU1VYMuovHDXcdNtRaz1rUdhW7gq+0So94G38MJtdfMnVyrrKkrzV498jSbbMc4ipAjPs23AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89b3f489ccedbaa00ecff68a275057d4015142d16b53a06d0ed4fd7db66e9c40","last_reissued_at":"2026-07-05T05:48:55.098439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:55.098439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recent Advances in Software Effort Estimation using Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Victor Uc-Cetina","submitted_at":"2023-03-06T20:25:16Z","abstract_excerpt":"An increasing number of software companies have already realized the importance of storing project-related data as valuable sources of information for training prediction models. Such kind of modeling opens the door for the implementation of tailored strategies to increase the accuracy in effort estimation of whole teams of engineers. In this article we review the most recent machine learning approaches used to estimate software development efforts for both, non-agile and agile methodologies. We analyze the benefits of adopting an agile methodology in terms of effort estimation possibilities, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.03482","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/2303.03482/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":"2303.03482","created_at":"2026-07-05T05:48:55.098495+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.03482v1","created_at":"2026-07-05T05:48:55.098495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.03482","created_at":"2026-07-05T05:48:55.098495+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGZ7JCOM5W5K","created_at":"2026-07-05T05:48:55.098495+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGZ7JCOM5W5KADWP","created_at":"2026-07-05T05:48:55.098495+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGZ7JCOM","created_at":"2026-07-05T05:48:55.098495+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.20439","citing_title":"When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions","ref_index":37,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q","json":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q.json","graph_json":"https://pith.science/api/pith-number/RGZ7JCOM5W5KADWP62FCOUCX2Q/graph.json","events_json":"https://pith.science/api/pith-number/RGZ7JCOM5W5KADWP62FCOUCX2Q/events.json","paper":"https://pith.science/paper/RGZ7JCOM"},"agent_actions":{"view_html":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q","download_json":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q.json","view_paper":"https://pith.science/paper/RGZ7JCOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.03482&json=true","fetch_graph":"https://pith.science/api/pith-number/RGZ7JCOM5W5KADWP62FCOUCX2Q/graph.json","fetch_events":"https://pith.science/api/pith-number/RGZ7JCOM5W5KADWP62FCOUCX2Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q/action/storage_attestation","attest_author":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q/action/author_attestation","sign_citation":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q/action/citation_signature","submit_replication":"https://pith.science/pith/RGZ7JCOM5W5KADWP62FCOUCX2Q/action/replication_record"}},"created_at":"2026-07-05T05:48:55.098495+00:00","updated_at":"2026-07-05T05:48:55.098495+00:00"}