{"id":"https://openalex.org/W3034465864","doi":"https://doi.org/10.1109/cvprw50498.2020.00170","title":"Leveraging combinatorial testing for safety-critical computer vision datasets","display_name":"Leveraging combinatorial testing for safety-critical computer vision datasets","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3034465864","doi":"https://doi.org/10.1109/cvprw50498.2020.00170","mag":"3034465864"},"language":"en","primary_location":{"id":"doi:10.1109/cvprw50498.2020.00170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw50498.2020.00170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","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/A5019488707","display_name":"Christoph Gladisch","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156055","display_name":"Robert Bosch (Netherlands)","ror":"https://ror.org/057aydj06","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210156055","https://openalex.org/I889804353"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Christoph Gladisch","raw_affiliation_strings":["Corporate Research, Robert Bosch GmbH"],"affiliations":[{"raw_affiliation_string":"Corporate Research, Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210156055"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080822166","display_name":"Christian Heinzemann","orcid":"https://orcid.org/0000-0003-2144-6215"},"institutions":[{"id":"https://openalex.org/I4210156055","display_name":"Robert Bosch (Netherlands)","ror":"https://ror.org/057aydj06","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210156055","https://openalex.org/I889804353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Christian Heinzemann","raw_affiliation_strings":["Corporate Research, Robert Bosch GmbH"],"affiliations":[{"raw_affiliation_string":"Corporate Research, Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210156055"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103023795","display_name":"Martin Herrmann","orcid":"https://orcid.org/0000-0002-7953-2354"},"institutions":[{"id":"https://openalex.org/I4210156055","display_name":"Robert Bosch (Netherlands)","ror":"https://ror.org/057aydj06","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210156055","https://openalex.org/I889804353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Martin Herrmann","raw_affiliation_strings":["Corporate Research, Robert Bosch GmbH"],"affiliations":[{"raw_affiliation_string":"Corporate Research, Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210156055"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048861762","display_name":"Matthias Woehrle","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156055","display_name":"Robert Bosch (Netherlands)","ror":"https://ror.org/057aydj06","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210156055","https://openalex.org/I889804353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Matthias Woehrle","raw_affiliation_strings":["Corporate Research, Robert Bosch GmbH"],"affiliations":[{"raw_affiliation_string":"Corporate Research, Robert Bosch GmbH","institution_ids":["https://openalex.org/I4210156055"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5019488707"],"corresponding_institution_ids":["https://openalex.org/I4210156055"],"apc_list":null,"apc_paid":null,"fwci":7.5959,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.97728938,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1314","last_page":"1321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.9830999970436096,"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/computer-science","display_name":"Computer science","score":0.8487943410873413},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6005828380584717},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5618579387664795},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5414562225341797},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.5155758261680603},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47918808460235596},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4564107060432434},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.45210856199264526},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3976907730102539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8487943410873413},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6005828380584717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5618579387664795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5414562225341797},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.5155758261680603},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47918808460235596},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4564107060432434},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.45210856199264526},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3976907730102539},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvprw50498.2020.00170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw50498.2020.00170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1524053243","https://openalex.org/W2075699551","https://openalex.org/W2128204165","https://openalex.org/W2328064123","https://openalex.org/W2340897893","https://openalex.org/W2578642488","https://openalex.org/W2605102758","https://openalex.org/W2767611112","https://openalex.org/W2775795276","https://openalex.org/W2782864149","https://openalex.org/W2809182766","https://openalex.org/W2894903065","https://openalex.org/W2965452642","https://openalex.org/W2971191047","https://openalex.org/W2975035192","https://openalex.org/W3002342835","https://openalex.org/W3011721937","https://openalex.org/W3082897186","https://openalex.org/W6701800711","https://openalex.org/W6747218270","https://openalex.org/W6752716229","https://openalex.org/W7033992032"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2170801710","https://openalex.org/W2952704802","https://openalex.org/W2741781807"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"approaches":[2],"have":[3],"gained":[4],"popularity":[5],"for":[6,36,53,62,80,105],"environment":[7],"perception":[8],"tasks":[9],"such":[10],"as":[11],"semantic":[12],"segmentation":[13],"and":[14,59,65,93,116,130],"object":[15],"detection":[16],"from":[17,48],"images.":[18],"However,":[19],"the":[20,54],"different":[21],"nature":[22],"of":[23,56],"a":[24,34,57,74,107],"data-driven":[25],"deep":[26],"neural":[27],"nets":[28],"(DNN)":[29],"to":[30,89,110],"conventional":[31],"software":[32,38,49],"is":[33,114],"challenge":[35],"practical":[37],"verification.":[39],"In":[40],"this":[41],"work,":[42],"we":[43,97],"show":[44,68,98],"how":[45,69,99],"existing":[46],"methods":[47],"engineering":[50],"provide":[51],"benefits":[52],"development":[55],"DNN":[58],"in":[60],"particular":[61],"dataset":[63],"design":[64],"analysis.":[66],"We":[67,121],"combinatorial":[70],"testing":[71],"based":[72],"on":[73,125],"domain":[75],"model":[76],"can":[77,102],"be":[78,103,118],"leveraged":[79],"generating":[81],"test":[82],"sets":[83],"providing":[84],"coverage":[85],"guarantees":[86],"with":[87],"respect":[88],"important":[90],"environmental":[91],"features":[92],"their":[94],"interaction.":[95],"Additionally,":[96],"our":[100,123],"approach":[101,124],"used":[104],"growing":[106],"dataset,":[108],"i.e.":[109],"identify":[111],"where":[112],"data":[113],"missing":[115],"should":[117],"collected":[119],"next.":[120],"evaluate":[122],"an":[126],"internal":[127],"use":[128],"case":[129],"two":[131],"public":[132],"datasets.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
