{"id":"https://openalex.org/W2075780741","doi":"https://doi.org/10.1145/2695664.2695959","title":"Evaluating defect prediction approaches using a massive set of metrics","display_name":"Evaluating defect prediction approaches using a massive set of metrics","publication_year":2015,"publication_date":"2015-04-13","ids":{"openalex":"https://openalex.org/W2075780741","doi":"https://doi.org/10.1145/2695664.2695959","mag":"2075780741"},"language":"en","primary_location":{"id":"doi:10.1145/2695664.2695959","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2695664.2695959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual ACM Symposium on Applied Computing","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/A5033602780","display_name":"Xuan Xiao","orcid":"https://orcid.org/0000-0003-1016-7544"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Xuan","raw_affiliation_strings":["Zhejiang University, China","ZHEJIANG UNIVERSITY, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"ZHEJIANG UNIVERSITY, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081036622","display_name":"David Lo","orcid":"https://orcid.org/0000-0002-4367-7201"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"David Lo","raw_affiliation_strings":["Singapore Management University, Singapore","Singapore Management University, SINGAPORE"],"affiliations":[{"raw_affiliation_string":"Singapore Management University, Singapore","institution_ids":["https://openalex.org/I79891267"]},{"raw_affiliation_string":"Singapore Management University, SINGAPORE","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006669765","display_name":"Xin Xia","orcid":"https://orcid.org/0000-0002-6302-3256"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xia","raw_affiliation_strings":["Zhejiang University, China","ZHEJIANG UNIVERSITY, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"ZHEJIANG UNIVERSITY, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100716456","display_name":"Yuan Tian","orcid":"https://orcid.org/0000-0002-2208-3893"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Singapore Management University, Singapore","Singapore Management University, SINGAPORE"],"affiliations":[{"raw_affiliation_string":"Singapore Management University, Singapore","institution_ids":["https://openalex.org/I79891267"]},{"raw_affiliation_string":"Singapore Management University, SINGAPORE","institution_ids":["https://openalex.org/I79891267"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033602780"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":4.9743,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.95237547,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1644","last_page":"1647"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9998000264167786,"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":0.9998000264167786,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.9911999702453613,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7584837079048157},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6501191854476929},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5960410237312317},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5929182171821594},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5879934430122375},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5748397707939148},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5473636388778687},{"id":"https://openalex.org/keywords/matthews-correlation-coefficient","display_name":"Matthews correlation coefficient","score":0.5348979234695435},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5338842272758484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5313395261764526},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47669145464897156},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.47111037373542786},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.451455682516098},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41604018211364746},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.24345585703849792},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19041216373443604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1323288083076477}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584837079048157},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6501191854476929},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5960410237312317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5929182171821594},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5879934430122375},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5748397707939148},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5473636388778687},{"id":"https://openalex.org/C164085508","wikidata":"https://www.wikidata.org/wiki/Q4811327","display_name":"Matthews correlation coefficient","level":3,"score":0.5348979234695435},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5338842272758484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5313395261764526},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47669145464897156},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.47111037373542786},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.451455682516098},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41604018211364746},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.24345585703849792},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19041216373443604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1323288083076477},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2695664.2695959","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2695664.2695959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-4081","is_oa":false,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4081&context=sis_research","pdf_url":null,"source":{"id":"https://openalex.org/S4377196871","display_name":"Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1145/2695664.2695959","raw_type":"Conference Proceeding Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W168970045","https://openalex.org/W1588282782","https://openalex.org/W1983030745","https://openalex.org/W2019338079","https://openalex.org/W2025700486","https://openalex.org/W2040900804","https://openalex.org/W2043387105","https://openalex.org/W2046830558","https://openalex.org/W2104882279","https://openalex.org/W2105776892","https://openalex.org/W2131229395","https://openalex.org/W2131712385","https://openalex.org/W2140190241","https://openalex.org/W2160958420","https://openalex.org/W2163614729","https://openalex.org/W2313307873","https://openalex.org/W2526529994","https://openalex.org/W2966207845","https://openalex.org/W4244763104"],"related_works":["https://openalex.org/W2394466068","https://openalex.org/W1987683558","https://openalex.org/W1036938216","https://openalex.org/W357196361","https://openalex.org/W2726838704","https://openalex.org/W4317422759","https://openalex.org/W4385770464","https://openalex.org/W4241599364","https://openalex.org/W4283216151","https://openalex.org/W2790921906"],"abstract_inverted_index":{"To":[0],"evaluate":[1,32,47,100,133],"the":[2,33,58,128,134,159,170,181],"performance":[3,34,59,135],"of":[4,28,35,60,69,136,169,180],"a":[5,25,66,121,154],"within-project":[6,61],"defect":[7,62,95,137,142],"prediction":[8,63,96,129,138],"approach,":[9],"people":[10],"normally":[11],"use":[12],"precision,":[13],"recall,":[14,161],"and":[15,44,87,114,123,164,173,175],"F-measure":[16,174],"scores.":[17],"However,":[18],"in":[19,94],"machine":[20],"learning":[21],"literature,":[22],"there":[23],"are":[24,91],"large":[26,67],"number":[27,68],"evaluation":[29,70,106],"metrics":[30,46,107],"to":[31,119],"an":[36,48],"algorithm,":[37],"(e.g.,":[38,108],"Matthews":[39],"Correlation":[40],"Coefficient,":[41],"G-means,":[42],"etc.),":[43],"these":[45,101],"approach":[49],"from":[50,144],"different":[51],"aspects.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56,99],"investigate":[57],"approaches":[64,76,103,130,139],"on":[65,104,140,166,177],"metrics.":[71],"We":[72,132],"choose":[73],"6":[74,102],"state-of-the-art":[75],"including":[77],"naive":[78],"Bayes,":[79],"decision":[80],"tree,":[81],"logistic":[82],"regression,":[83],"kNN,":[84],"random":[85],"forest":[86],"Bayesian":[88,151],"network":[89,152],"which":[90],"widely":[92],"used":[93],"literature.":[97],"And":[98],"14":[105],"G-mean,":[109],"F-measure,":[110],"balance,":[111],"MCC,":[112],"J-coefficient,":[113],"AUC).":[115],"Our":[116],"goal":[117],"is":[118],"explore":[120],"practical":[122],"sophisticated":[124],"way":[125],"for":[126],"evaluating":[127],"comprehensively.":[131],"10":[141,171,182],"datasets":[143],"PROMISE":[145],"repository.":[146],"The":[147],"results":[148],"show":[149],"that":[150],"achieves":[153,158],"noteworthy":[155],"performance.":[156],"It":[157],"best":[160],"FN-R,":[162],"G-mean1":[163],"balance":[165],"9":[167],"out":[168,179],"datasets,":[172],"J-coefficient":[176],"7":[178],"datasets.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}