{"id":"https://openalex.org/W7155362393","doi":"https://doi.org/10.48550/arxiv.2604.20742","title":"Evaluating Software Defect Prediction Models via the Area Under the ROC Curve Can Be Misleading","display_name":"Evaluating Software Defect Prediction Models via the Area Under the ROC Curve Can Be Misleading","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155362393","doi":"https://doi.org/10.48550/arxiv.2604.20742"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20742","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":null,"license_id":null,"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.2604.20742","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068567905","display_name":"Luigi Lavazza","orcid":"https://orcid.org/0000-0002-5226-4337"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lavazza, Luigi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092161942","display_name":"Gabriele Rotoloni","orcid":"https://orcid.org/0000-0003-2046-0090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rotoloni, Gabriele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051028027","display_name":"Sandro Morasca","orcid":"https://orcid.org/0000-0003-4598-7024"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morasca, Sandro","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":0.9229999780654907,"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":0.9229999780654907,"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.015599999576807022,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.00930000003427267,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.9092000126838684},{"id":"https://openalex.org/keywords/false-positive-rate","display_name":"False positive rate","score":0.5766000151634216},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4810999929904938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39469999074935913},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.364300012588501},{"id":"https://openalex.org/keywords/area-under-curve","display_name":"Area under curve","score":0.36410000920295715},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.34630000591278076}],"concepts":[{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.9092000126838684},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.5766000151634216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5231000185012817},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4810999929904938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4228000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3725999891757965},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C3020225094","wikidata":"https://www.wikidata.org/wiki/Q80091","display_name":"Area under curve","level":3,"score":0.36410000920295715},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C202632270","wikidata":"https://www.wikidata.org/wiki/Q7798106","display_name":"Threshold model","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20742","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20742","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background:":[0],"Receiver":[1],"Operating":[2],"Characteristic":[3],"(ROC)":[4],"curves":[5,74,113,122,213],"are":[6,83,92,103,200,217],"widely":[7],"used":[8,169],"to":[9,128,174,219],"evaluate":[10,149],"the":[11,24,58,63,77,80,125,144,161,188,193,203,222],"performance":[12,37,64],"of":[13,40,65,79,143,181,197,225],"Software":[14],"Defect":[15],"Prediction":[16],"(SDP)":[17],"models":[18],"that":[19,26,167,186],"estimate":[20],"module":[21,28],"fault-proneness,":[22],"i.e.,":[23],"probability":[25],"a":[27,35,66,151,198],"is":[29,116],"faulty.":[30],"A":[31,178],"ROC":[32,59,73,81,112,121,212],"curve":[33],"maps":[34],"model's":[36,205],"in":[38],"terms":[39],"True":[41,134,189],"Positive":[42,46,135,139,190,195],"Rate":[43,47,136,140,191,196],"and":[44,88,114,137,156,192],"False":[45,138,194],"for":[48,206],"any":[49],"possible":[50,70,208],"threshold":[51,129],"set":[52],"on":[53,111],"fault-proneness.":[54],"The":[55],"Area":[56],"Under":[57],"Curve":[60],"(AUC)":[61],"summarizes":[62],"model":[67,108,152,199],"across":[68],"all":[69,207,221],"thresholds.":[71,209],"Traditionally,":[72],"completely":[75],"above":[76],"bisector":[78],"space":[82],"considered":[84],"better":[85,159,201],"than":[86,160,202],"random,":[87],"high":[89,179],"AUC":[90,115,182],"values":[91],"associated":[93],"with":[94],"good":[95],"performance.":[96],"Aim:":[97],"We":[98,119,131,165],"investigate":[99],"whether":[100,150],"these":[101],"beliefs":[102],"correct,":[104],"hence":[105],"if":[106],"SDP":[107,226],"evaluation":[109,170],"based":[110],"reliable.":[117],"Method:":[118],"decorate":[120],"by":[123],"highlighting":[124],"points":[126],"corresponding":[127],"values.":[130],"also":[132],"represent":[133],"as":[141],"functions":[142],"threshold.":[145],"Thus,":[146],"we":[147],"can":[148],"classifies":[153],"both":[154,187],"faulty":[155],"non-faulty":[157],"modules":[158],"random":[162,204],"model.":[163],"Results:":[164],"show":[166],"commonly":[168],"criteria":[171],"may":[172],"lead":[173],"wrong":[175],"conclusions.":[176],"Conclusions:":[177],"value":[180],"does":[183],"not":[184],"guarantee":[185],"Either":[210],"decorated":[211],"or":[214],"alternative":[215],"representations":[216],"needed":[218],"appreciate":[220],"relevant":[223],"aspects":[224],"models.":[227]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-24T00:00:00"}
