{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:OORTA4MX4Y6U2HQTJZPIHCDJC5","short_pith_number":"pith:OORTA4MX","canonical_record":{"source":{"id":"1609.01759","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-09-06T20:56:39Z","cross_cats_sorted":[],"title_canon_sha256":"908e61689bafcdf3349fa90a488caf920a28714cfb8494b711f7a2025ab68200","abstract_canon_sha256":"c1949822aadfcae321f7ba0a65406d3be9295fcdefa1166beaebe90b69e928ab"},"schema_version":"1.0"},"canonical_sha256":"73a3307197e63d4d1e134e5e8388691769cc50e89e15f19da5c0087e21f15a92","source":{"kind":"arxiv","id":"1609.01759","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.01759","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1609.01759v1","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.01759","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"OORTA4MX4Y6U","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"OORTA4MX4Y6U2HQT","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"OORTA4MX","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:OORTA4MX4Y6U2HQTJZPIHCDJC5","target":"record","payload":{"canonical_record":{"source":{"id":"1609.01759","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-09-06T20:56:39Z","cross_cats_sorted":[],"title_canon_sha256":"908e61689bafcdf3349fa90a488caf920a28714cfb8494b711f7a2025ab68200","abstract_canon_sha256":"c1949822aadfcae321f7ba0a65406d3be9295fcdefa1166beaebe90b69e928ab"},"schema_version":"1.0"},"canonical_sha256":"73a3307197e63d4d1e134e5e8388691769cc50e89e15f19da5c0087e21f15a92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:53.173941Z","signature_b64":"VL2YHgCLbpeHTyw3xODuLCCAjkQKdjgm0ryuGNLsCQho/6BfgIXlrAYGdxw8mgSwHNlbLWuqyyq1cpzOwtyRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73a3307197e63d4d1e134e5e8388691769cc50e89e15f19da5c0087e21f15a92","last_reissued_at":"2026-05-18T01:04:53.173388Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:53.173388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.01759","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UE5wKvXtcwK9EdF27cQ/OXWzuUXctny+Qe6K9UezdfvZO0j8OAAKi8KJt4k2M/nCASzXiijd/VqDtah3A6t9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:43:00.854484Z"},"content_sha256":"2b8d2b17935e9ffaf028376268666b7b7742f9e817663c968bf668aa58cce305","schema_version":"1.0","event_id":"sha256:2b8d2b17935e9ffaf028376268666b7b7742f9e817663c968bf668aa58cce305"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:OORTA4MX4Y6U2HQTJZPIHCDJC5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tuning for Software Analytics: is it Really Necessary?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Tim Menzies, Wei Fu, Xipeng Shen","submitted_at":"2016-09-06T20:56:39Z","abstract_excerpt":"Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the \"black art\" of data mining is setting the tunings that control the miner. Objective:We seek simple, automatic, and very effective method for finding those tunings. Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as anoptimizer to explore the tuning space (as a first "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.01759","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CMboqtSMExAmuHXzq7bL5tgA5kUPz43ubgSOB/lYwB9m0G5zwVbv0+ZBdA1uGuAXXQKc7RyoeNuOQ/KNRMycAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:43:00.854843Z"},"content_sha256":"fadaa0aaeceaffd86335f521021733c66b6b925e990ba68d571e7f30d4143856","schema_version":"1.0","event_id":"sha256:fadaa0aaeceaffd86335f521021733c66b6b925e990ba68d571e7f30d4143856"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/bundle.json","state_url":"https://pith.science/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T04:43:00Z","links":{"resolver":"https://pith.science/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5","bundle":"https://pith.science/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/bundle.json","state":"https://pith.science/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OORTA4MX4Y6U2HQTJZPIHCDJC5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:OORTA4MX4Y6U2HQTJZPIHCDJC5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c1949822aadfcae321f7ba0a65406d3be9295fcdefa1166beaebe90b69e928ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-09-06T20:56:39Z","title_canon_sha256":"908e61689bafcdf3349fa90a488caf920a28714cfb8494b711f7a2025ab68200"},"schema_version":"1.0","source":{"id":"1609.01759","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.01759","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1609.01759v1","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.01759","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"OORTA4MX4Y6U","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"OORTA4MX4Y6U2HQT","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"OORTA4MX","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:fadaa0aaeceaffd86335f521021733c66b6b925e990ba68d571e7f30d4143856","target":"graph","created_at":"2026-05-18T01:04:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the \"black art\" of data mining is setting the tunings that control the miner. Objective:We seek simple, automatic, and very effective method for finding those tunings. Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as anoptimizer to explore the tuning space (as a first ","authors_text":"Tim Menzies, Wei Fu, Xipeng Shen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-09-06T20:56:39Z","title":"Tuning for Software Analytics: is it Really Necessary?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.01759","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2b8d2b17935e9ffaf028376268666b7b7742f9e817663c968bf668aa58cce305","target":"record","created_at":"2026-05-18T01:04:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c1949822aadfcae321f7ba0a65406d3be9295fcdefa1166beaebe90b69e928ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2016-09-06T20:56:39Z","title_canon_sha256":"908e61689bafcdf3349fa90a488caf920a28714cfb8494b711f7a2025ab68200"},"schema_version":"1.0","source":{"id":"1609.01759","kind":"arxiv","version":1}},"canonical_sha256":"73a3307197e63d4d1e134e5e8388691769cc50e89e15f19da5c0087e21f15a92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73a3307197e63d4d1e134e5e8388691769cc50e89e15f19da5c0087e21f15a92","first_computed_at":"2026-05-18T01:04:53.173388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:53.173388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VL2YHgCLbpeHTyw3xODuLCCAjkQKdjgm0ryuGNLsCQho/6BfgIXlrAYGdxw8mgSwHNlbLWuqyyq1cpzOwtyRCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:53.173941Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.01759","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b8d2b17935e9ffaf028376268666b7b7742f9e817663c968bf668aa58cce305","sha256:fadaa0aaeceaffd86335f521021733c66b6b925e990ba68d571e7f30d4143856"],"state_sha256":"6d0e5db20cb3a2a1455a28778ca0d51bb4cee651bd5e55179dcfb7554e50303c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fJUwBCTg/yAke6JCrOzg2CkgGu0rzQhEKV6d3QDoE2dQaNJ68AiAoRYKShS3tngMMPrcBjvqt5IxJFze+LNIAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T04:43:00.857024Z","bundle_sha256":"20dbd7f3fbd38b40a32db06c45bde26eceeaaab4d08f9974a0a9c05ae15c4985"}}