{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ATG3GFK2DZHZYSQKBTEL4O2KDO","short_pith_number":"pith:ATG3GFK2","schema_version":"1.0","canonical_sha256":"04cdb3155a1e4f9c4a0a0cc8be3b4a1bb7ec3d18c174379a315309e82b16e679","source":{"kind":"arxiv","id":"1704.04773","version":1},"attestation_state":"computed","paper":{"title":"Approximate Backbone Based Multilevel Algorithm for Next Release Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"He Jiang, Jifeng Xuan, Zhilei Ren","submitted_at":"2017-04-16T13:13:28Z","abstract_excerpt":"The next release problem (NRP) aims to effectively select software requirements in order to acquire maximum customer profits. As an NP-hard problem in software requirement engineering, NRP lacks efficient approximate algorithms for large scale instances. The backbone is a new tool for tackling large scale NP-hard problems in recent years. In this paper, we employ the backbone to design high performance approximate algorithms for large scale NRP instances. Firstly we show that it is NP-hard to obtain the backbone of NRP. Then, we illustrate by fitness landscape analysis that the backbone can be"},"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":"1704.04773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2017-04-16T13:13:28Z","cross_cats_sorted":[],"title_canon_sha256":"4310a6a8259065a315575c2d5595c2fb160707f5836c5749f3f6d0821cae99c4","abstract_canon_sha256":"6a522e486644a3bf7c6358e1f808be2f770f8434a4835f49ee82f828ac258b57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:17.771932Z","signature_b64":"7sP4DQXXmuVAKNiNPV8ymT2g49rbtMqVRLoWiY/p+7GFgL9F8tA88AOb5MRWGGGPr3aVt4xGdhTmXwcelqiqCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04cdb3155a1e4f9c4a0a0cc8be3b4a1bb7ec3d18c174379a315309e82b16e679","last_reissued_at":"2026-05-18T00:46:17.771277Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:17.771277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Backbone Based Multilevel Algorithm for Next Release Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"He Jiang, Jifeng Xuan, Zhilei Ren","submitted_at":"2017-04-16T13:13:28Z","abstract_excerpt":"The next release problem (NRP) aims to effectively select software requirements in order to acquire maximum customer profits. As an NP-hard problem in software requirement engineering, NRP lacks efficient approximate algorithms for large scale instances. The backbone is a new tool for tackling large scale NP-hard problems in recent years. In this paper, we employ the backbone to design high performance approximate algorithms for large scale NRP instances. Firstly we show that it is NP-hard to obtain the backbone of NRP. Then, we illustrate by fitness landscape analysis that the backbone can be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04773","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":""},"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":"1704.04773","created_at":"2026-05-18T00:46:17.771399+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.04773v1","created_at":"2026-05-18T00:46:17.771399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04773","created_at":"2026-05-18T00:46:17.771399+00:00"},{"alias_kind":"pith_short_12","alias_value":"ATG3GFK2DZHZ","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"ATG3GFK2DZHZYSQK","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"ATG3GFK2","created_at":"2026-05-18T12:31:08.081275+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO","json":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO.json","graph_json":"https://pith.science/api/pith-number/ATG3GFK2DZHZYSQKBTEL4O2KDO/graph.json","events_json":"https://pith.science/api/pith-number/ATG3GFK2DZHZYSQKBTEL4O2KDO/events.json","paper":"https://pith.science/paper/ATG3GFK2"},"agent_actions":{"view_html":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO","download_json":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO.json","view_paper":"https://pith.science/paper/ATG3GFK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.04773&json=true","fetch_graph":"https://pith.science/api/pith-number/ATG3GFK2DZHZYSQKBTEL4O2KDO/graph.json","fetch_events":"https://pith.science/api/pith-number/ATG3GFK2DZHZYSQKBTEL4O2KDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO/action/storage_attestation","attest_author":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO/action/author_attestation","sign_citation":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO/action/citation_signature","submit_replication":"https://pith.science/pith/ATG3GFK2DZHZYSQKBTEL4O2KDO/action/replication_record"}},"created_at":"2026-05-18T00:46:17.771399+00:00","updated_at":"2026-05-18T00:46:17.771399+00:00"}