{"id":"https://openalex.org/W2955438753","doi":"https://doi.org/10.1109/raise.2019.00016","title":"An Explainable Deep Model for Defect Prediction","display_name":"An Explainable Deep Model for Defect Prediction","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2955438753","doi":"https://doi.org/10.1109/raise.2019.00016","mag":"2955438753"},"language":"en","primary_location":{"id":"doi:10.1109/raise.2019.00016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/raise.2019.00016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)","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/A5024524002","display_name":"Jack Humphreys","orcid":null},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jack Humphreys","raw_affiliation_strings":["University of Wollongong"],"affiliations":[{"raw_affiliation_string":"University of Wollongong","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017181940","display_name":"Hoa Khanh Dam","orcid":"https://orcid.org/0000-0003-4246-0526"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hoa Khanh Dam","raw_affiliation_strings":["University of Wollongong"],"affiliations":[{"raw_affiliation_string":"University of Wollongong","institution_ids":["https://openalex.org/I204824540"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024524002"],"corresponding_institution_ids":["https://openalex.org/I204824540"],"apc_list":null,"apc_paid":null,"fwci":2.0349,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89760893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9994000196456909,"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":0.9994000196456909,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9797000288963318,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials 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":0.7369152307510376},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.7052843570709229},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6402339935302734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5437177419662476},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4658815264701843},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4383935332298279},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.42609700560569763},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4190458357334137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4048663377761841},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32655954360961914},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.2802857458591461},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15801817178726196}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369152307510376},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7052843570709229},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6402339935302734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437177419662476},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4658815264701843},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4383935332298279},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.42609700560569763},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4190458357334137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4048663377761841},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32655954360961914},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.2802857458591461},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15801817178726196},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/raise.2019.00016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/raise.2019.00016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1973445088","https://openalex.org/W2033418259","https://openalex.org/W2064675550","https://openalex.org/W2074805796","https://openalex.org/W2104364184","https://openalex.org/W2112658968","https://openalex.org/W2143637886","https://openalex.org/W2146338950","https://openalex.org/W2151666086","https://openalex.org/W2158744032","https://openalex.org/W2170207476","https://openalex.org/W2360967250","https://openalex.org/W2394841101","https://openalex.org/W2470673105","https://openalex.org/W2963998044","https://openalex.org/W4206600618","https://openalex.org/W4226065182","https://openalex.org/W4297747548","https://openalex.org/W4385245566","https://openalex.org/W4398786193","https://openalex.org/W6685064470","https://openalex.org/W6739901393","https://openalex.org/W6749669830"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2142795561","https://openalex.org/W3155418658","https://openalex.org/W4389518428","https://openalex.org/W4282583532","https://openalex.org/W4256076151"],"abstract_inverted_index":{"Self":[0],"attention":[1],"transformer":[2],"encoders":[3],"represent":[4],"an":[5],"effective":[6],"method":[7],"for":[8,53],"sequence":[9],"to":[10,59,78,91],"class":[11],"prediction":[12,42,64,113],"tasks":[13],"as":[14],"they":[15],"can":[16],"disentangle":[17],"long":[18],"distance":[19],"dependencies":[20],"and":[21,43,86,99],"have":[22],"many":[23,45],"regularising":[24],"effects.":[25],"We":[26],"achieve":[27],"results":[28],"substantially":[29],"better":[30],"than":[31],"state":[32],"of":[33,62,102,108],"the":[34,60,63,88,106],"art":[35],"in":[36,67,118],"one":[37],"such":[38],"task,":[39],"namely,":[40],"defect":[41,112],"with":[44],"added":[46],"benefits.":[47],"Existing":[48],"techniques":[49],"do":[50],"not":[51],"normalise":[52],"correlations":[54],"that":[55],"are":[56],"inversely":[57],"proportional":[58],"usefulness":[61],"but":[65],"do,":[66],"fact,":[68],"go":[69],"further,":[70],"specifically":[71],"exploiting":[72],"these":[73],"features":[74],"which":[75,109],"is":[76,83],"tantamount":[77],"data":[79],"leakage.":[80],"Our":[81],"model":[82],"end-to-end":[84],"trainable":[85],"has":[87,110],"potential":[89,100],"capability":[90],"explain":[92],"its":[93],"prediction.":[94],"This":[95],"explainability":[96],"provides":[97],"insights":[98],"causes":[101],"a":[103],"model's":[104],"decisions,":[105],"absence":[107],"stopped":[111],"from":[114],"gaining":[115],"any":[116],"traction":[117],"industry.":[119]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
