{"id":"https://openalex.org/W4385893344","doi":"https://doi.org/10.1109/sera57763.2023.10197800","title":"TIPICAL - Type Inference for Python In Critical Accuracy Level","display_name":"TIPICAL - Type Inference for Python In Critical Accuracy Level","publication_year":2023,"publication_date":"2023-05-23","ids":{"openalex":"https://openalex.org/W4385893344","doi":"https://doi.org/10.1109/sera57763.2023.10197800"},"language":"en","primary_location":{"id":"doi:10.1109/sera57763.2023.10197800","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/sera57763.2023.10197800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","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/A5092801976","display_name":"Jonathan Elkobi","orcid":null},"institutions":[{"id":"https://openalex.org/I2800935791","display_name":"UC San Diego Health System","ror":"https://ror.org/01kbfgm16","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2800935791"]},{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]},{"id":"https://openalex.org/I160856358","display_name":"University of San Diego","ror":"https://ror.org/03jbbze48","country_code":"US","type":"education","lineage":["https://openalex.org/I160856358"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jonathan Elkobi","raw_affiliation_strings":["UC San Diego,GPS,San Diego,USA","GPS, UC San Diego, San Diego, USA"],"affiliations":[{"raw_affiliation_string":"UC San Diego,GPS,San Diego,USA","institution_ids":["https://openalex.org/I2800935791","https://openalex.org/I160856358"]},{"raw_affiliation_string":"GPS, UC San Diego, San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012533726","display_name":"Bernd Gruner","orcid":"https://orcid.org/0000-0002-4177-2993"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernd Gruner","raw_affiliation_strings":["Institute of Data Science,German Aerospace Center,Jena,Germany","German Aerospace Center, Institute of Data Science, Jena, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science,German Aerospace Center,Jena,Germany","institution_ids":["https://openalex.org/I2898391981"]},{"raw_affiliation_string":"German Aerospace Center, Institute of Data Science, Jena, Germany","institution_ids":["https://openalex.org/I2898391981"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071262516","display_name":"Tim Sonnekalb","orcid":"https://orcid.org/0000-0002-0067-1790"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tim Sonnekalb","raw_affiliation_strings":["Institute of Data Science,German Aerospace Center,Jena,Germany","German Aerospace Center, Institute of Data Science, Jena, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science,German Aerospace Center,Jena,Germany","institution_ids":["https://openalex.org/I2898391981"]},{"raw_affiliation_string":"German Aerospace Center, Institute of Data Science, Jena, Germany","institution_ids":["https://openalex.org/I2898391981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055950864","display_name":"Clemens-Alexander Brust","orcid":"https://orcid.org/0000-0001-5419-1998"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Clemens-Alexander Brust","raw_affiliation_strings":["Institute of Data Science,German Aerospace Center,Jena,Germany","German Aerospace Center, Institute of Data Science, Jena, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Data Science,German Aerospace Center,Jena,Germany","institution_ids":["https://openalex.org/I2898391981"]},{"raw_affiliation_string":"German Aerospace Center, Institute of Data Science, Jena, Germany","institution_ids":["https://openalex.org/I2898391981"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5092801976"],"corresponding_institution_ids":["https://openalex.org/I160856358","https://openalex.org/I2800935791","https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17435144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"21"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9886999726295471,"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":0.9884999990463257,"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/inference","display_name":"Inference","score":0.7836935520172119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7597455382347107},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6889503002166748},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.5804365873336792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5701742768287659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5274384617805481},{"id":"https://openalex.org/keywords/type-inference","display_name":"Type inference","score":0.49443864822387695},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4841804802417755},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.47593072056770325},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.4377533197402954},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.16293179988861084}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7836935520172119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7597455382347107},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6889503002166748},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.5804365873336792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5701742768287659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5274384617805481},{"id":"https://openalex.org/C198370458","wikidata":"https://www.wikidata.org/wiki/Q586459","display_name":"Type inference","level":3,"score":0.49443864822387695},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4841804802417755},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.47593072056770325},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4377533197402954},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.16293179988861084},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sera57763.2023.10197800","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/sera57763.2023.10197800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","raw_type":"proceedings-article"},{"id":"pmh:oai:elib.dlr.de:196617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/SERA57763.2023.10197800>.","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W2086141560","https://openalex.org/W2535174441","https://openalex.org/W2617588282","https://openalex.org/W2903469802","https://openalex.org/W2991250775","https://openalex.org/W3018033251","https://openalex.org/W3100869085","https://openalex.org/W3105735055","https://openalex.org/W3160955651","https://openalex.org/W3173453854","https://openalex.org/W3177164493","https://openalex.org/W3177617320","https://openalex.org/W3194752080","https://openalex.org/W4284687350","https://openalex.org/W4287819720","https://openalex.org/W4384009784","https://openalex.org/W4386366369","https://openalex.org/W6636510571","https://openalex.org/W6771332894","https://openalex.org/W6776707598","https://openalex.org/W6795668794","https://openalex.org/W6798041529"],"related_works":["https://openalex.org/W4297831890","https://openalex.org/W2804371217","https://openalex.org/W2963764498","https://openalex.org/W2068383718","https://openalex.org/W4246881098","https://openalex.org/W2112150205","https://openalex.org/W1550049051","https://openalex.org/W1557199137","https://openalex.org/W4287025197","https://openalex.org/W3196270186"],"abstract_inverted_index":{"Type":[0],"inference":[1,58,115],"methods":[2],"based":[3],"on":[4],"deep":[5,77],"learning":[6,79],"are":[7,53],"becoming":[8],"increasingly":[9],"popular":[10],"as":[11,26,42],"they":[12],"aim":[13],"to":[14,37,55,61,111],"compensate":[15],"for":[16],"the":[17,56,112,131],"drawbacks":[18],"of":[19,133],"static":[20],"and":[21,65,101,106,125],"dynamic":[22],"analysis":[23],"approaches,":[24],"such":[25,41],"high":[27,94],"uncertainty.":[28],"However,":[29],"their":[30],"practical":[31],"application":[32],"is":[33],"still":[34],"debatable":[35],"due":[36],"several":[38],"intrinsic":[39],"issues":[40],"code":[43],"from":[44],"different":[45,122],"software":[46,123],"domains":[47,124],"will":[48],"involve":[49],"data":[50,91,104,126],"types":[51,92,105],"that":[52,75,85],"unknown":[54,100],"type":[57,114,127],"system.In":[59],"order":[60],"overcome":[62],"these":[63],"problems":[64],"gain":[66],"high-confidence":[67],"predictions,":[68],"we":[69,119],"thus":[70],"present":[71],"TIPICAL,":[72],"a":[73],"method":[74,87,116],"combines":[76],"similarity":[78],"with":[80],"novelty":[81],"detection.":[82],"We":[83],"show":[84],"our":[86,134],"can":[88],"better":[89],"predict":[90],"in":[93],"confidence":[95],"by":[96],"successfully":[97],"filtering":[98],"out":[99],"inaccurate":[102],"predicted":[103],"achieving":[107],"higher":[108],"F1":[109],"scores":[110],"state-of-the-art":[113],"Type4Py.":[117],"Additionally,":[118],"investigate":[120],"how":[121],"frequencies":[128],"may":[129],"affect":[130],"results":[132],"method.":[135]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
