{"id":"https://openalex.org/W7140368476","doi":"https://doi.org/10.48550/arxiv.2603.23769","title":"Empirical Characterization of Logging Smells in Machine Learning Code","display_name":"Empirical Characterization of Logging Smells in Machine Learning Code","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140368476","doi":"https://doi.org/10.48550/arxiv.2603.23769"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23769","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024280538","display_name":"Patrick Loic Foalem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Foalem, Patrick Loic","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001086221","display_name":"L\u00e9uson Da Silva","orcid":"https://orcid.org/0000-0002-9086-9038"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Da Silva, Leuson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130664668","display_name":"Foutse Khomh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khomh, Foutse","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130656231","display_name":"Heng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Heng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102966564","display_name":"Ettore Merlo","orcid":"https://orcid.org/0000-0002-1436-6076"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Merlo, Ettore","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.19609999656677246,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.19609999656677246,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.10090000182390213,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.09040000289678574,"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/logging","display_name":"Logging","score":0.9409000277519226},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5519999861717224},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.35569998621940613},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.3109999895095825},{"id":"https://openalex.org/keywords/code-smell","display_name":"Code smell","score":0.29820001125335693}],"concepts":[{"id":"https://openalex.org/C125620115","wikidata":"https://www.wikidata.org/wiki/Q845249","display_name":"Logging","level":2,"score":0.9409000277519226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5655999779701233},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39309999346733093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3109999895095825},{"id":"https://openalex.org/C133237599","wikidata":"https://www.wikidata.org/wiki/Q2295111","display_name":"Code smell","level":5,"score":0.29820001125335693},{"id":"https://openalex.org/C2777989319","wikidata":"https://www.wikidata.org/wiki/Q597393","display_name":"Illegal logging","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.24619999527931213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Logging":[0,142,169],"plays":[1],"a":[2,21,54,63,123,155,194],"central":[3],"role":[4],"in":[5,11,49,106,111],"ensuring":[6],"reproducibility,":[7,159],"observability,":[8],"and":[9,35,52,70,95,109,113,136,150,161,170,196,203,209],"reliability":[10],"machine":[12],"learning":[13],"(ML)":[14],"systems.":[15],"While":[16],"logging":[17,27,47,58,76,87,102,191,212],"is":[18],"generally":[19],"considered":[20],"good":[22],"engineering":[23,202],"practice,":[24],"poorly":[25],"designed":[26],"can":[28],"negatively":[29],"affect":[30],"experiment":[31],"tracking,":[32],"security,":[33,90],"debugging,":[34],"system":[36,201],"performance.":[37],"In":[38],"this":[39,80],"paper,":[40],"we":[41,82,121],"present":[42],"an":[43],"empirical":[44],"study":[45],"of":[46,56,66,75,86,199,211],"smells":[48,88,103,135],"ML":[50,68,107,127,200],"projects":[51],"propose":[53],"taxonomy":[55],"ML-specific":[57],"smell":[59],"types.":[60],"We":[61,178],"conducted":[62,122],"large-scale":[64],"analysis":[65],"444":[67],"repositories":[69],"manually":[71],"labeled":[72,182],"2,448":[73],"instances":[74],"smells.":[77],"Based":[78],"on":[79,158],"analysis,":[81],"identified":[83,134],"12":[84],"categories":[85],"spanning":[89],"metric":[91],"management,":[92],"configuration,":[93],"verbosity,":[94],"context-related":[96],"issues.":[97,213],"Our":[98,188],"results":[99],"show":[100],"that":[101,138],"are":[104],"widespread":[105],"systems":[108],"vary":[110],"frequency":[112],"manifestation":[114],"across":[115],"projects.":[116],"To":[117],"assess":[118],"practical":[119],"relevance,":[120],"survey":[124],"with":[125,132],"27":[126],"practitioners.":[128],"Most":[129],"respondents":[130],"agreed":[131],"the":[133],"reported":[137],"several":[139],"types,":[140],"including":[141],"Sensitive":[143],"Data,":[144],"Metric":[145],"Overwrite,":[146],"Missing":[147],"Hyperparameter":[148],"Logging,":[149,172],"Log":[151],"Without":[152],"Context,":[153],"have":[154],"strong":[156],"impact":[157],"maintainability,":[160],"trustworthiness.":[162],"Other":[163],"smells,":[164],"such":[165],"as":[166,175,193],"Heavy":[167],"Data":[168],"Print-based":[171],"were":[173],"perceived":[174],"more":[176],"context-dependent.":[177],"publicly":[179],"release":[180],"our":[181],"dataset":[183],"to":[184],"support":[185],"future":[186],"research.":[187],"findings":[189],"highlight":[190],"quality":[192],"critical":[195],"underexplored":[197],"aspect":[198],"open":[204],"opportunities":[205],"for":[206],"automated":[207],"detection":[208],"repair":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}
