{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PIHLY3OIZOP2OFFLUPQGVFNJA6","short_pith_number":"pith:PIHLY3OI","schema_version":"1.0","canonical_sha256":"7a0ebc6dc8cb9fa714aba3e06a95a90784b1e097e650ad119ddb82bb3b56dd98","source":{"kind":"arxiv","id":"1704.00234","version":2},"attestation_state":"computed","paper":{"title":"Transfer Learning for Improving Model Predictions in Highly Configurable Software","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Christian K\\\"astner, Miguel Velez, Norbert Siegmund, Pooyan Jamshidi, Prasad Kawthekar","submitted_at":"2017-04-01T22:50:42Z","abstract_excerpt":"Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Natur"},"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":"1704.00234","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2017-04-01T22:50:42Z","cross_cats_sorted":[],"title_canon_sha256":"9f9fc83bac54ff13cec884c1d11fc7a8f712f2fc5bf1925318db507e27008efc","abstract_canon_sha256":"55e5cf1f1513fcbb1984ffed70ffd8d6b0e3a5e0cd2dac6817d8db67201b451c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:00.184607Z","signature_b64":"CWqUw79tpBdBUOJc8aRiBqfCc5csbSjZXHQjtoEzcAn9GTEtmZtOEbpRNWnO0KX+tIV8fOshatYpWtJA1NpRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a0ebc6dc8cb9fa714aba3e06a95a90784b1e097e650ad119ddb82bb3b56dd98","last_reissued_at":"2026-05-18T00:46:00.183835Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:00.183835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transfer Learning for Improving Model Predictions in Highly Configurable Software","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Christian K\\\"astner, Miguel Velez, Norbert Siegmund, Pooyan Jamshidi, Prasad Kawthekar","submitted_at":"2017-04-01T22:50:42Z","abstract_excerpt":"Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Natur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00234","kind":"arxiv","version":2},"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":"1704.00234","created_at":"2026-05-18T00:46:00.183983+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.00234v2","created_at":"2026-05-18T00:46:00.183983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00234","created_at":"2026-05-18T00:46:00.183983+00:00"},{"alias_kind":"pith_short_12","alias_value":"PIHLY3OIZOP2","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PIHLY3OIZOP2OFFL","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PIHLY3OI","created_at":"2026-05-18T12:31:37.085036+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/PIHLY3OIZOP2OFFLUPQGVFNJA6","json":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6.json","graph_json":"https://pith.science/api/pith-number/PIHLY3OIZOP2OFFLUPQGVFNJA6/graph.json","events_json":"https://pith.science/api/pith-number/PIHLY3OIZOP2OFFLUPQGVFNJA6/events.json","paper":"https://pith.science/paper/PIHLY3OI"},"agent_actions":{"view_html":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6","download_json":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6.json","view_paper":"https://pith.science/paper/PIHLY3OI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.00234&json=true","fetch_graph":"https://pith.science/api/pith-number/PIHLY3OIZOP2OFFLUPQGVFNJA6/graph.json","fetch_events":"https://pith.science/api/pith-number/PIHLY3OIZOP2OFFLUPQGVFNJA6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6/action/storage_attestation","attest_author":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6/action/author_attestation","sign_citation":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6/action/citation_signature","submit_replication":"https://pith.science/pith/PIHLY3OIZOP2OFFLUPQGVFNJA6/action/replication_record"}},"created_at":"2026-05-18T00:46:00.183983+00:00","updated_at":"2026-05-18T00:46:00.183983+00:00"}