{"id":"https://openalex.org/W6963562982","doi":"https://doi.org/10.21227/dxef-tx53","title":"Feature Representation Repository for Defect Prediction","display_name":"Feature Representation Repository for Defect Prediction","publication_year":2022,"publication_date":"2022-08-29","ids":{"openalex":"https://openalex.org/W6963562982","doi":"https://doi.org/10.21227/dxef-tx53"},"language":"en","primary_location":{"id":"doi:10.21227/dxef-tx53","is_oa":true,"landing_page_url":"https://doi.org/10.21227/dxef-tx53","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/dxef-tx53","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gupta, Mansi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Mansi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Rajnish, Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajnish, Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Bhattacharjee, Vandana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhattacharjee, Vandana","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":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.7067999839782715},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5751000046730042},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.519599974155426},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47679999470710754},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.43939998745918274},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40310001373291016},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3846000134944916},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.3418000042438507},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.32580000162124634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531999945640564},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7067999839782715},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5751000046730042},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.519599974155426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5083000063896179},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4235000014305115},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3540000021457672},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3125},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C103275481","wikidata":"https://www.wikidata.org/wiki/Q6787889","display_name":"Matrix representation","level":3,"score":0.31139999628067017},{"id":"https://openalex.org/C149091818","wikidata":"https://www.wikidata.org/wiki/Q2429814","display_name":"Software system","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C101317890","wikidata":"https://www.wikidata.org/wiki/Q940053","display_name":"Software maintenance","level":4,"score":0.2718000113964081},{"id":"https://openalex.org/C76518257","wikidata":"https://www.wikidata.org/wiki/Q271680","display_name":"Software framework","level":5,"score":0.27149999141693115},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C1009929","wikidata":"https://www.wikidata.org/wiki/Q179550","display_name":"Software bug","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.2506999969482422},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/dxef-tx53","is_oa":true,"landing_page_url":"https://doi.org/10.21227/dxef-tx53","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/dxef-tx53","is_oa":true,"landing_page_url":"https://doi.org/10.21227/dxef-tx53","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Dataset"},"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":{"The":[0,217,302],"software":[1,12,27,85,215],"engineering":[2],"community":[3],"is":[4,15,32,174,221],"working":[5],"to":[6,10,46,58,62,79,95,104,162,182,213,298],"develop":[7],"reliable":[8],"metrics":[9],"improve":[11],"quality.":[13],"It":[14],"estimated":[16],"that":[17,128,306],"understanding":[18],"the":[19,26,36,41,48,81,97,117,150,154,164,168,178,183,300,307],"source":[20,49],"code":[21],"accounts":[22],"for":[23,157,177],"60%":[24],"of":[25,38,68,84,167],"maintenance":[28],"effort.":[29],"Cognitive":[30,132],"informatics":[31],"important":[33],"in":[34,56,146,329],"quantifying":[35],"degree":[37],"difficulty":[39],"or":[40],"efforts":[42],"made":[43,94],"by":[44,76,235],"developers":[45],"understand":[47],"code.":[50],"Several":[51],"empirical":[52],"studies":[53],"were":[54,233,245],"conducted":[55],"2003":[57],"assign":[59],"cognitive":[60,72,82],"weights":[61,73],"each":[63,120,261],"possible":[64],"basic":[65],"control":[66],"structure":[67],"software,":[69],"and":[70,119,148,180,196,205,226,239,242,259,264,278,292,325],"these":[71],"are":[74,296],"used":[75,297],"several":[77],"researchers":[78],"evaluate":[80,299],"complexity":[83],"systems.":[86],"In":[87,108,160],"this":[88],"paper,":[89],"an":[90,125],"effort":[91],"has":[92],"been":[93],"categorize":[96],"Control":[98],"Flow":[99],"Graphs":[100],"(CFGs)":[101],"nodes":[102],"according":[103],"their":[105],"node":[106,143,155,171],"features.":[107],"our":[109,190,236],"case,":[110],"we":[111,129],"extracted":[112],"seven":[113],"unique":[114,121],"features":[115],"from":[116],"program,":[118],"feature":[122,144,165],"was":[123],"assigned":[124],"integer":[126],"value":[127,145],"evaluated":[130],"through":[131],"Complexity":[133],"Measures":[134],"(CCMs).":[135],"We":[136,188],"then":[137,175,197],"incorporated":[138],"CCMs'":[139],"results":[140,304],"as":[141,314],"a":[142,158,170],"CFGs":[147],"generated":[149],"same":[151],"based":[152],"on":[153],"connectivity":[156],"graph.":[159],"order":[161],"obtain":[163],"representation":[166],"graph,":[169],"vector":[172],"matrix":[173],"created":[176],"graph":[179],"passed":[181],"Graph":[184],"Convolutional":[185,206],"Network":[186,201,208],"(GCN).":[187],"prepared":[189],"data":[191],"sets":[192],"using":[193],"GCN":[194],"output":[195],"built":[198],"Deep":[199],"Neural":[200,207],"Defect":[202,209],"Prediction":[203,210],"(DNN-DP)":[204],"(CNN-DP)":[211],"models":[212,309],"predict":[214],"defects.":[216],"Python":[218,231],"programming":[219],"language":[220],"used,":[222],"along":[223],"with":[224,252],"Keras":[225],"TensorFlow.":[227],"Three":[228],"hundred":[229],"twenty":[230],"programs":[232,267],"written":[234],"talented":[237],"UG":[238],"PG":[240],"students,":[241],"all":[243,330],"experiments":[244],"carried":[246],"out":[247],"during":[248],"laboratory":[249],"classes.":[250],"Together":[251],"three":[253,272],"skilled":[254],"lab":[255],"programmers,":[256],"they":[257],"compiled":[258],"ran":[260],"individual":[262],"program":[263],"detected":[265],"defect/no-defect":[266],"before":[268],"categorizing":[269],"them":[270],"into":[271],"different":[273],"classes,":[274],"namely":[275],"Simple,":[276],"Medium,":[277],"Complex":[279],"programs.":[280],"Accuracy,":[281],"Receiver":[282],"Operating":[283],"Characteristics":[284],"(ROC),":[285],"Area":[286],"Under":[287],"Curve":[288],"(AUC),":[289],"F-measure,":[290],"Precision":[291],"hyper-parameter":[293],"tuning":[294],"procedures":[295],"approaches.":[301],"experimental":[303],"show":[305],"proposed":[308],"outperformed":[310],"state-of-the-art":[311],"methods":[312],"such":[313],"Nave":[315],"Bayes":[316],"(NB),":[317],"Decision":[318],"Tree":[319],"(DT),":[320],"Support":[321],"Vector":[322],"Machine":[323],"(SVM),":[324],"Random":[326],"Forest":[327],"(RF)":[328],"evaluation":[331],"criteria.":[332]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
