{"id":"https://openalex.org/W4392248313","doi":"https://doi.org/10.1109/icce59016.2024.10444275","title":"A Method for Selecting Training Data Using Doc2Vec for Automatic Test Cases Generation","display_name":"A Method for Selecting Training Data Using Doc2Vec for Automatic Test Cases Generation","publication_year":2024,"publication_date":"2024-01-06","ids":{"openalex":"https://openalex.org/W4392248313","doi":"https://doi.org/10.1109/icce59016.2024.10444275"},"language":"en","primary_location":{"id":"doi:10.1109/icce59016.2024.10444275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111256585","display_name":"Yuto Fujita","orcid":null},"institutions":[{"id":"https://openalex.org/I104946051","display_name":"Nihon University","ror":"https://ror.org/05jk51a88","country_code":"JP","type":"education","lineage":["https://openalex.org/I104946051"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yuto Fujita","raw_affiliation_strings":["Nihon University,Graduate School of Engineering,Koriyama,Japan","Graduate School of Engineering, Nihon University, Koriyama, Japan"],"affiliations":[{"raw_affiliation_string":"Nihon University,Graduate School of Engineering,Koriyama,Japan","institution_ids":["https://openalex.org/I104946051"]},{"raw_affiliation_string":"Graduate School of Engineering, Nihon University, Koriyama, Japan","institution_ids":["https://openalex.org/I104946051"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065729863","display_name":"Kiyoshi Ueda","orcid":"https://orcid.org/0000-0001-8051-8966"},"institutions":[{"id":"https://openalex.org/I104946051","display_name":"Nihon University","ror":"https://ror.org/05jk51a88","country_code":"JP","type":"education","lineage":["https://openalex.org/I104946051"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kiyoshi Ueda","raw_affiliation_strings":["Nihon University,Graduate School of Engineering,Koriyama,Japan","Graduate School of Engineering, Nihon University, Koriyama, Japan"],"affiliations":[{"raw_affiliation_string":"Nihon University,Graduate School of Engineering,Koriyama,Japan","institution_ids":["https://openalex.org/I104946051"]},{"raw_affiliation_string":"Graduate School of Engineering, Nihon University, Koriyama, Japan","institution_ids":["https://openalex.org/I104946051"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111256585"],"corresponding_institution_ids":["https://openalex.org/I104946051"],"apc_list":null,"apc_paid":null,"fwci":2.4927,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90039233,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T12127","display_name":"Software System Performance and Reliability","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T14025","display_name":"Educational Technology and Assessment","score":0.9642999768257141,"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/computer-science","display_name":"Computer science","score":0.8192477226257324},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.6771690845489502},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5755181312561035},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5557954907417297},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4383432865142822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4001348316669464},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3693190813064575},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3214200735092163},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.19908392429351807}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8192477226257324},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.6771690845489502},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5755181312561035},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5557954907417297},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4383432865142822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4001348316669464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693190813064575},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3214200735092163},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.19908392429351807},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce59016.2024.10444275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W132293876","https://openalex.org/W2131744502","https://openalex.org/W2789031204","https://openalex.org/W2995607718","https://openalex.org/W3161913203","https://openalex.org/W4400613548","https://openalex.org/W6605337486","https://openalex.org/W6679775712","https://openalex.org/W6870146672"],"related_works":["https://openalex.org/W4394050964","https://openalex.org/W2551249631","https://openalex.org/W209733029","https://openalex.org/W2891480213","https://openalex.org/W2099971677","https://openalex.org/W2991483587","https://openalex.org/W3118953353","https://openalex.org/W2158542502","https://openalex.org/W1997978958","https://openalex.org/W133774893"],"abstract_inverted_index":{"In":[0,41],"the":[1,9,46,77,104,114,117,122,125,148,151],"development":[2,12],"of":[3,16,25,48,81,116,132,150,157],"large-scale":[4,133],"communication":[5,134],"software,":[6],"due":[7],"to":[8,20,67,76,146],"increase":[10],"in":[11],"cost":[13],"and":[14,28,84,102],"shortage":[15],"labor,":[17],"a":[18,65,92,154],"method":[19,66,93,119,127,152],"automatically":[21],"generate":[22],"test":[23,50,82],"cases":[24,51],"system":[26],"testing":[27,30],"acceptance":[29],"from":[31],"requirement":[32,55,70,78,97,108,129,143,158],"specification":[33,56,71,79,98,109,130,144,159],"documents":[34,57,72,131,145],"using":[35,100,124],"machine":[36,59],"learning":[37,60],"has":[38],"been":[39],"studied.":[40],"this":[42],"study,":[43],"we":[44],"improve":[45],"accuracy":[47,123],"automatic":[49],"generation":[52],"by":[53,120],"selecting":[54],"for":[58],"training":[61,88],"data.":[62,89],"We":[63,90,112],"studied":[64],"select":[68],"structured":[69],"with":[73],"high":[74],"similarity":[75,105],"document":[80,99,110],"data":[83],"use":[85],"them":[86],"as":[87],"propose":[91],"that":[94],"vectorize":[95],"each":[96,107],"Doc2Vec":[101],"calculate":[103],"between":[106],"vector.":[111],"evaluate":[113],"effectiveness":[115,149],"proposed":[118,126],"measuring":[121],"on":[128,140,153],"software.":[135],"Experiments":[136],"were":[137],"also":[138],"conducted":[139],"different":[141],"systems":[142],"confirm":[147],"wide":[155],"variety":[156],"documents.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}