{"id":"https://openalex.org/W4409103769","doi":"https://doi.org/10.1109/acoit62457.2024.10939610","title":"Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison","display_name":"Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison","publication_year":2024,"publication_date":"2024-09-06","ids":{"openalex":"https://openalex.org/W4409103769","doi":"https://doi.org/10.1109/acoit62457.2024.10939610"},"language":"en","primary_location":{"id":"doi:10.1109/acoit62457.2024.10939610","doi":"https://doi.org/10.1109/acoit62457.2024.10939610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acoit62457.2024.10939610","pdf_url":null,"source":{"id":"https://openalex.org/S7407096866","display_name":"Asian Conference on Intelligent Technologies (ACOIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Asian Conference on Intelligent Technologies (ACOIT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5115408428","display_name":"Sai Krishna Gunda","orcid":"https://orcid.org/0009-0007-8547-8298","observed_orcids":["https://orcid.org/0009-0007-8547-8298"]},"institutions":[{"id":"https://openalex.org/I2799939184","display_name":"Home Depot (United States)","ror":"https://ror.org/031603425","country_code":"US","type":"company","lineage":["https://openalex.org/I2799939184"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sai Krishna Gunda","raw_affiliation_strings":["The Home Depot,Software Engineer,Atlanta,Georgia,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Home Depot,Software Engineer,Atlanta,Georgia,USA","institution_ids":["https://openalex.org/I2799939184"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5115408428"],"corresponding_institution_ids":["https://openalex.org/I2799939184"],"apc_list":null,"apc_paid":null,"fwci":11.3073,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.98897194,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9994999766349792,"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":0.9994999766349792,"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":0.00019999999494757503,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":9.999999747378752e-05,"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/software-defect-prediction","display_name":"software defect prediction","score":1.0},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.8899999856948853},{"id":"https://openalex.org/keywords/lightgbm","display_name":"LightGBM","score":1.0},{"id":"https://openalex.org/keywords/catboost","display_name":"CatBoost","score":0.8709999918937683},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.9750000238418579},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.9589999914169312},{"id":"https://openalex.org/keywords/classifier-comparison","display_name":"classifier comparison","score":0.8629999756813049},{"id":"https://openalex.org/keywords/precision-recall-trade-off","display_name":"precision-recall trade-off","score":0.7239999771118164},{"id":"https://openalex.org/keywords/defect-detection","display_name":"defect detection","score":0.49799999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7675992250442505},{"id":"https://openalex.org/C1009929","wikidata":"https://www.wikidata.org/wiki/Q179550","display_name":"Software bug","level":3,"score":0.608718991279602},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5588953495025635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4817502498626709},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.47697147727012634},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.3594959080219269},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.19628992676734924}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acoit62457.2024.10939610","doi":"https://doi.org/10.1109/acoit62457.2024.10939610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acoit62457.2024.10939610","pdf_url":null,"source":{"id":"https://openalex.org/S7407096866","display_name":"Asian Conference on Intelligent Technologies (ACOIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Asian Conference on Intelligent Technologies (ACOIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1981055536","https://openalex.org/W2048456683","https://openalex.org/W2080521990","https://openalex.org/W2105776892","https://openalex.org/W2787986668","https://openalex.org/W3008381189","https://openalex.org/W3119533254","https://openalex.org/W4221057950","https://openalex.org/W4297997901","https://openalex.org/W4395678722"],"related_works":["https://openalex.org/W4416963838","https://openalex.org/W4361733625","https://openalex.org/W4409427564","https://openalex.org/W4410705961","https://openalex.org/W4405306154","https://openalex.org/W4417053683","https://openalex.org/W4415709329","https://openalex.org/W7203926370","https://openalex.org/W4414183915","https://openalex.org/W7208712567"],"abstract_inverted_index":{"This":[0,142],"study":[1],"used":[2,153],"the":[3,46,93,108,123,145,163,186],"JM1":[4],"dataset":[5],"of":[6,58,66,80,110,131,165,188],"software":[7],"module":[8],"metrics":[9],"and":[10,37,52,60,82,89,140,149,185],"tested":[11],"various":[12],"machine":[13],"learning":[14],"classifiers":[15],"to":[16,144,168],"detect":[17],"defective":[18],"modules.":[19,114],"Classifiers":[20],"used:":[21],"Gradient":[22,90],"Boosting,":[23],"AdaBoost":[24,88],"XGBoost":[25,44,139,150],"LightGBM":[26,70,148],"Catboost":[27],"The":[28],"models":[29,103,125],"were":[30],"assessed":[31],"using":[32],"accuracy,":[33],"precision,":[34],"recall,":[35,53,98],"F1-score,":[36],"cross-validation":[38,56],"metrics.":[39],"Our":[40],"findings":[41],"indicated":[42],"that":[43,101,121,147],"achieved":[45],"most":[47],"balanced":[48],"performance":[49,75],"between":[50],"precision":[51,95],"with":[54,76,133,162],"a":[55,61,73,77,134],"mean":[57,79],"0.8437":[59],"macro":[62],"average":[63],"$F":[64],"1$-score":[65],"$\\mathbf{0.":[67],"6":[68],"0}$.":[69],"also":[71],"gave":[72],"good":[74],"CV":[78],"0.8466":[81],"an":[83],"F1":[84],"score":[85],"as":[86,154],"0.57.":[87],"Boosting":[91],"had":[92],"lowest":[94],"but":[96],"highest":[97],"which":[99],"indicates":[100],"these":[102],"have":[104],"some":[105],"issues":[106],"in":[107,129,157],"area":[109],"finding":[111],"all":[112],"faulty":[113],"Regarding":[115],"defect":[116,158],"prediction,":[117],"it":[118],"was":[119],"observed":[120],"for":[122],"selected":[124],"CatBoost":[126],"performed":[127],"best":[128],"terms":[130],"accuracy":[132,161],"bit":[135],"lower":[136],"recall":[137],"than":[138],"LightGBM.":[141],"leads":[143],"conclusion":[146],"can":[151,180],"be":[152],"competitive":[155],"alternatives":[156],"detection,":[159],"balancing":[160],"quality":[164],"being":[166],"able":[167],"catch":[169],"real":[170],"defects.":[171],"To":[172],"further":[173],"improve":[174],"fault":[175],"identification":[176],"performance,":[177],"future":[178],"research":[179],"concentrate":[181],"on":[182],"hyperparameter":[183],"optimization":[184],"integration":[187],"other":[189],"data":[190],"sources.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":5}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-04-03T00:00:00"}