{"id":"https://openalex.org/W4315629811","doi":"https://doi.org/10.1109/globecom48099.2022.10001093","title":"MMOS: Multi-Staged Mutation Operator Scheduling for Deep Learning Library Testing","display_name":"MMOS: Multi-Staged Mutation Operator Scheduling for Deep Learning Library Testing","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315629811","doi":"https://doi.org/10.1109/globecom48099.2022.10001093"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10001093","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10001093","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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/A5100633035","display_name":"Junqiang Li","orcid":"https://orcid.org/0000-0002-8454-996X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junqiang Li","raw_affiliation_strings":["School of Information and Communication Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020292808","display_name":"Senyi Li","orcid":"https://orcid.org/0000-0003-1597-2702"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Senyi Li","raw_affiliation_strings":["School of Information and Communication Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005259359","display_name":"Jiawei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiawei Wu","raw_affiliation_strings":["School of Information and Communication Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109772212","display_name":"Long Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long Luo","raw_affiliation_strings":["School of Information and Communication Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101709412","display_name":"Yang Bai","orcid":"https://orcid.org/0000-0002-7322-5556"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Bai","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","No.30 Institute of CETC, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"No.30 Institute of CETC, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101507232","display_name":"Hongfang Yu","orcid":"https://orcid.org/0000-0002-5219-1780"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongfang Yu","raw_affiliation_strings":["School of Information and Communication Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5191,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65216008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6103","last_page":"6108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9958999752998352,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.6523006558418274},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.5800363421440125},{"id":"https://openalex.org/keywords/mutation","display_name":"Mutation","score":0.5672992467880249},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5523658990859985},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.52886563539505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46299228072166443},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.4597257971763611},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39719700813293457},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2149001955986023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15743085741996765},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.11116853356361389}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523006558418274},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.5800363421440125},{"id":"https://openalex.org/C501734568","wikidata":"https://www.wikidata.org/wiki/Q42918","display_name":"Mutation","level":3,"score":0.5672992467880249},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5523658990859985},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.52886563539505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46299228072166443},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.4597257971763611},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39719700813293457},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2149001955986023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15743085741996765},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.11116853356361389},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10001093","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10001093","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G8155370207","display_name":null,"funder_award_id":"6142111200303","funder_id":"https://openalex.org/F4320326932","funder_display_name":"National Key Laboratory of Science and Technology on Communications"}],"funders":[{"id":"https://openalex.org/F4320326932","display_name":"National Key Laboratory of Science and Technology on Communications","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2168405694","https://openalex.org/W2885847999","https://openalex.org/W2904932877","https://openalex.org/W2954903132","https://openalex.org/W2963064278","https://openalex.org/W2971058209","https://openalex.org/W3101584909","https://openalex.org/W3104663419","https://openalex.org/W3160029089","https://openalex.org/W3196351123","https://openalex.org/W3199980055","https://openalex.org/W4285356139","https://openalex.org/W6731155804","https://openalex.org/W6753377161","https://openalex.org/W6766204418","https://openalex.org/W6800448998"],"related_works":["https://openalex.org/W2264067234","https://openalex.org/W3124243301","https://openalex.org/W1571502335","https://openalex.org/W1589409554","https://openalex.org/W2759038785","https://openalex.org/W2172232600","https://openalex.org/W3123876860","https://openalex.org/W3124172198","https://openalex.org/W2046181650","https://openalex.org/W2543854215"],"abstract_inverted_index":{"The":[0,133],"rapid":[1],"development":[2],"of":[3,24,42,52,100,124,190],"deep":[4,10],"learning":[5,11],"(DL)":[6],"technology":[7],"has":[8],"made":[9],"libraries":[12,26,157],"such":[13],"as":[14],"Tensor":[15],"Flow":[16],"widely":[17,154],"used":[18,155],"in":[19,103,152,177,181,187],"practice.":[20],"However,":[21,55],"the":[22,50,85,98,104,108,122,166],"complexity":[23],"DL":[25,35,105,156,191],"inevitably":[27],"leads":[28],"to":[29,45,81,96],"multiple":[30],"vulnerabilities.":[31],"Recently,":[32],"research":[33,59],"on":[34,84],"library":[36],"testing":[37],"generally":[38],"designs":[39],"a":[40,90,129],"variety":[41],"mutation":[43,66,76,92,110,115],"operators":[44,67,77],"generate":[46],"new":[47],"models":[48],"from":[49],"perspective":[51],"model":[53],"mutation.":[54],"we":[56,88,127],"find":[57],"these":[58],"efforts":[60],"do":[61],"not":[62],"consider":[63],"that":[64,136],"different":[65,69],"have":[68],"vulnerability":[70,101,188],"mining":[71,102,189],"efficiencies,":[72],"thus":[73],"treating":[74],"all":[75],"equally":[78],"can":[79],"lead":[80],"inefficiency.":[82],"Based":[83],"above":[86],"observation,":[87],"design":[89],"novel":[91],"operator":[93,111,116],"scheduling":[94],"strategy":[95,113],"improve":[97],"efficiency":[99,123],"library,":[106],"including":[107,158],"multi-staged":[109],"selection":[112],"and":[114,147,162,179],"energy":[117],"allocation":[118],"strategy.":[119,168],"To":[120],"evaluate":[121],"our":[125],"work,":[126],"implement":[128],"prototype":[130],"called":[131],"MMOS.":[132],"results":[134],"show":[135],"MMOS":[137,171,184,195],"finds":[138,172,196],"six":[139],"more":[140,144,149,198],"crash":[141],"bugs,":[142],"two":[143],"NaN":[145],"bugs":[146],"one":[148],"inconsistency":[150],"bug":[151],"four":[153],"TensorFlow,":[159],"Theano,":[160],"CNTK":[161],"MXNet":[163,178],"compared":[164],"with":[165],"random":[167],"Among":[169],"them,":[170],"5":[173],"previously":[174],"unknown":[175],"vulnerabilities":[176,199],"1":[180],"CNTK.":[182],"Moreover,":[183],"outperforms":[185],"LEMON":[186],"libraries.":[192],"In":[193],"total,":[194],"13":[197],"than":[200],"LEMON.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}