{"id":"https://openalex.org/W3085062472","doi":"https://doi.org/10.1145/3387904.3389263","title":"Duplicate Bug Report Detection Using Dual-Channel Convolutional Neural Networks","display_name":"Duplicate Bug Report Detection Using Dual-Channel Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-07-13","ids":{"openalex":"https://openalex.org/W3085062472","doi":"https://doi.org/10.1145/3387904.3389263","mag":"3085062472"},"language":"en","primary_location":{"id":"doi:10.1145/3387904.3389263","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3387904.3389263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th International Conference on Program Comprehension","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/A5071493261","display_name":"Jianjun He","orcid":"https://orcid.org/0000-0002-6942-8457"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianjun He","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101467167","display_name":"Ling Xu","orcid":"https://orcid.org/0000-0002-7203-511X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Xu","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070783668","display_name":"Meng Yan","orcid":"https://orcid.org/0000-0002-9538-9121"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Yan","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"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/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xin Xia","raw_affiliation_strings":["Monash University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100639534","display_name":"Yan Lei","orcid":"https://orcid.org/0000-0003-4504-6806"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Lei","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071493261"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":11.1362,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.98392515,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"117","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9735999703407288,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9664999842643738,"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/computer-science","display_name":"Computer science","score":0.8068637847900391},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6616432070732117},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5410922169685364},{"id":"https://openalex.org/keywords/software-bug","display_name":"Software bug","score":0.5136079788208008},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4502352178096771},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.41406333446502686},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3635268807411194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32986482977867126},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3218701481819153},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1972896158695221},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.1761247217655182}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8068637847900391},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6616432070732117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5410922169685364},{"id":"https://openalex.org/C1009929","wikidata":"https://www.wikidata.org/wiki/Q179550","display_name":"Software bug","level":3,"score":0.5136079788208008},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4502352178096771},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.41406333446502686},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3635268807411194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32986482977867126},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3218701481819153},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1972896158695221},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.1761247217655182},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3387904.3389263","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3387904.3389263","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th International Conference on Program Comprehension","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1494332322","https://openalex.org/W1560851690","https://openalex.org/W1880262756","https://openalex.org/W1977282352","https://openalex.org/W1979841934","https://openalex.org/W1981109290","https://openalex.org/W1985266020","https://openalex.org/W1994857824","https://openalex.org/W1999910737","https://openalex.org/W2006700268","https://openalex.org/W2039741433","https://openalex.org/W2053158571","https://openalex.org/W2061557777","https://openalex.org/W2125587588","https://openalex.org/W2127577307","https://openalex.org/W2130146200","https://openalex.org/W2133108681","https://openalex.org/W2139270835","https://openalex.org/W2149427297","https://openalex.org/W2156833313","https://openalex.org/W2165022036","https://openalex.org/W2167630669","https://openalex.org/W2170240176","https://openalex.org/W2285056549","https://openalex.org/W2287648897","https://openalex.org/W2402268235","https://openalex.org/W2470959079","https://openalex.org/W2478191067","https://openalex.org/W2527936178","https://openalex.org/W2545762469","https://openalex.org/W2548378638","https://openalex.org/W2735665712","https://openalex.org/W2736131177","https://openalex.org/W2761752002","https://openalex.org/W2767852858","https://openalex.org/W2809554815","https://openalex.org/W2887004133","https://openalex.org/W2892362240","https://openalex.org/W3031968991"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W2062195135","https://openalex.org/W4225147082"],"abstract_inverted_index":{"Developers":[0],"rely":[1],"on":[2,105,172],"bug":[3,9,17,36,39,46,53,59,69,82,100,115,128,131,147,162,226],"reports":[4,10,60,163],"to":[5,21,32,136,140,156],"fix":[6],"bugs.":[7],"The":[8,214],"are":[11,134,164],"usually":[12],"stored":[13],"and":[14,186,191],"managed":[15],"in":[16,37,93],"tracking":[18,40,47],"systems.":[19],"Due":[20],"the":[22,34,38,45,142,153,192,205],"different":[23,26,30],"expression":[24],"habits,":[25],"reporters":[27],"may":[28],"use":[29,211],"expressions":[31],"describe":[33],"same":[35],"system.":[41],"As":[42],"a":[43,63,98,113,137,159,187],"result,":[44],"system":[48],"often":[49],"contains":[50],"many":[51],"duplicate":[52,58,81,99,165,225],"reports.":[54,129,148],"Automatically":[55],"detecting":[56],"these":[57],"would":[61],"save":[62],"large":[64,174],"amount":[65],"of":[66,161,194],"effort":[67],"for":[68,80,224],"analysis.":[70],"Prior":[71],"studies":[72],"have":[73],"found":[74],"that":[75,217],"deep-learning":[76,212],"technique":[77],"is":[78,222],"effective":[79,223],"report":[83,101,116,132,227],"detection.":[84,228],"Inspired":[85],"by":[86],"recent":[87],"Natural":[88],"Language":[89],"Processing":[90],"(NLP)":[91],"research,":[92],"this":[94],"paper,":[95],"we":[96],"propose":[97],"detection":[102],"approach":[103,151,171],"based":[104],"Dual-Channel":[106],"Convolutional":[107],"Neural":[108],"Networks":[109],"(DC-CNN).":[110],"We":[111,168],"present":[112],"novel":[114],"pair":[117,160],"representation,":[118],"i.e.,":[119],"dual-channel":[120,219],"matrix":[121,220],"through":[122],"concatenating":[123],"two":[124,206],"single-channel":[125],"matrices":[126],"representing":[127],"Such":[130,202],"pairs":[133],"fed":[135],"CNN":[138],"model":[139],"capture":[141],"correlated":[143],"semantic":[144],"relationships":[145],"between":[146],"Then,":[149],"our":[150,170,218],"uses":[152],"association":[154],"features":[155],"classify":[157],"whether":[158],"or":[166],"not.":[167],"evaluate":[169],"three":[173,177],"datasets":[175],"from":[176],"open-source":[178],"projects,":[179],"including":[180],"Open":[181],"Office,":[182],"Eclipse,":[183],"Net":[184],"Beans":[185],"larger":[188],"combined":[189],"dataset,":[190],"accuracy":[193],"classification":[195],"reaches":[196],"0.9429,":[197],"0.9685,":[198],"0.9534,":[199],"0.9552":[200],"respectively.":[201],"performance":[203],"outperforms":[204],"state-of-the-art":[207],"approaches":[208],"which":[209],"also":[210],"techniques.":[213],"results":[215],"indicate":[216],"representation":[221]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2025-10-10T00:00:00"}