{"id":"https://openalex.org/W7164010646","doi":"https://doi.org/10.48550/arxiv.2606.08096","title":"Identifying unique developers in OSS projects: A family of models","display_name":"Identifying unique developers in OSS projects: A family of models","publication_year":2026,"publication_date":"2026-06-06","ids":{"openalex":"https://openalex.org/W7164010646","doi":"https://doi.org/10.48550/arxiv.2606.08096"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.08096","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138208988","display_name":"Ruoyu Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078111792","display_name":"Alexander Bakhtin","orcid":"https://orcid.org/0000-0003-3513-7253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bakhtin, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138239257","display_name":"Matteo Esposito","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esposito, Matteo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138286801","display_name":"Davide Taibi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taibi, Davide","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015576503","display_name":"Valentina Lenarduzzi","orcid":"https://orcid.org/0000-0003-0511-5133"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lenarduzzi, Valentina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.5674999952316284,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.5674999952316284,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.08669999986886978,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12034","display_name":"Digital and Cyber Forensics","score":0.03909999877214432,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.6565999984741211},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6288999915122986},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6025000214576721},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.593500018119812},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5845000147819519},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5788999795913696},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5590999722480774},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5476999878883362}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404000163078308},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.6565999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6385999917984009},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.593500018119812},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5845000147819519},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5788999795913696},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5590999722480774},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5476999878883362},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4823000133037567},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41100001335144043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.30070000886917114},{"id":"https://openalex.org/C119054055","wikidata":"https://www.wikidata.org/wiki/Q1576681","display_name":"Indel","level":5,"score":0.3001999855041504},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.08096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Organizational":[0],"and":[1,19,21,45,52,95,108,111,118,133],"logical":[2],"coupling":[3,33],"metrics":[4],"require":[5],"reliable":[6],"identification":[7],"of":[8,87,100,121],"unique":[9],"developers.":[10],"In":[11],"OSS,":[12],"commit":[13],"metadata":[14],"is":[15,37],"limited":[16],"to":[17,41,53,82],"names":[18],"emails,":[20],"the":[22,128],"same":[23],"developer":[24,50],"may":[25],"appear":[26],"under":[27],"multiple":[28],"aliases,":[29],"which":[30,125],"can":[31],"distort":[32],"measurements":[34],"if":[35],"de-duplication":[36,51],"missing.":[38],"We":[39,66,113],"aim":[40],"build":[42],"a":[43,58,71,84,115,119],"scalable":[44],"accurate":[46],"pipeline":[47],"for":[48,135],"OSS":[49,137],"provide":[54],"guidance":[55],"on":[56,61],"choosing":[57],"model":[59],"based":[60],"precision":[62,104],"vs.":[63],"computational":[64],"effort.":[65],"use":[67],"Indel":[68],"similarity":[69],"as":[70],"baseline,":[72],"then":[73],"run":[74],"an":[75],"LLM-assisted":[76],"matching":[77],"process":[78],"with":[79,106],"manual":[80],"validation":[81],"create":[83],"large":[85],"dataset":[86,117],"duplicate":[88],"identities.":[89],"Using":[90],"this":[91],"dataset,":[92],"we":[93],"train":[94],"compare":[96],"classical":[97],"ML":[98],"models":[99],"different":[101],"complexity,":[102],"evaluating":[103],"along":[105],"training":[107],"inference":[109],"time":[110],"energy.":[112],"expect":[114],"high-quality":[116],"benchmark":[120],"approaches":[122],"that":[123],"clarifies":[124],"solutions":[126],"offer":[127],"best":[129],"trade-off":[130],"between":[131],"accuracy":[132],"cost":[134],"large-scale":[136],"mining.":[138]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}