{"id":"https://openalex.org/W7163408365","doi":"https://doi.org/10.48550/arxiv.2606.03640","title":"Can AI be Easy? Lessons Learned from the EZR.py Toolkit","display_name":"Can AI be Easy? Lessons Learned from the EZR.py Toolkit","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7163408365","doi":"https://doi.org/10.48550/arxiv.2606.03640"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.03640","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137779024","display_name":"Tim Menzies","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Menzies, Tim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123624370","display_name":"Srinath Srinivasan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srinivasan, Srinath","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/T12535","display_name":"Machine Learning and Data Classification","score":0.3093999922275543,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.3093999922275543,"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/T10260","display_name":"Software Engineering Research","score":0.0877000018954277,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.05660000070929527,"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/code-refactoring","display_name":"Code refactoring","score":0.927299976348877},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.7049999833106995},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/source-lines-of-code","display_name":"Source lines of code","score":0.4546000063419342},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4530999958515167},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.4047999978065491},{"id":"https://openalex.org/keywords/regression-testing","display_name":"Regression testing","score":0.3774000108242035}],"concepts":[{"id":"https://openalex.org/C152752567","wikidata":"https://www.wikidata.org/wiki/Q116877","display_name":"Code refactoring","level":3,"score":0.927299976348877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7240999937057495},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.7049999833106995},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.625},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.45750001072883606},{"id":"https://openalex.org/C199519371","wikidata":"https://www.wikidata.org/wiki/Q942695","display_name":"Source lines of code","level":3,"score":0.4546000063419342},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4530999958515167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43070000410079956},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3785000145435333},{"id":"https://openalex.org/C161821725","wikidata":"https://www.wikidata.org/wiki/Q917415","display_name":"Regression testing","level":5,"score":0.3774000108242035},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.3499000072479248},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C105446022","wikidata":"https://www.wikidata.org/wiki/Q445962","display_name":"Legacy system","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C2778241615","wikidata":"https://www.wikidata.org/wiki/Q83303","display_name":"Fortran","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8639374375343323,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Much":[0],"recent":[1],"press":[2],"claims":[3],"that":[4,53,96],"developers":[5],"no":[6],"longer":[7],"need":[8],"to":[9,37,88,109,115],"read":[10],"code.":[11],"We":[12,191],"disagree,":[13],"at":[14,234],"least":[15],"within":[16,194],"the":[17,32,104,132,139,157,195],"domain":[18],"of":[19,26,45,173,197,209],"tabular":[20,75,134,198],"software-engineering":[21],"(SE)":[22],"optimization":[23,136],"tasks:":[24],"rows":[25],"$x$":[27],"and":[28,60,69,84,90,120,160,178,202,212],"$y$":[29,33],"values":[30,34],"where":[31],"are":[35,102,189],"expensive":[36],"obtain.":[38],"As":[39],"evidence":[40],"we":[41],"present":[42],"400":[43],"lines":[44,118],"EZR.py,":[46],"a":[47,116,121,206],"Python":[48],"toolkit":[49],"(no":[50],"heavy":[51],"dependencies)":[52],"implements":[54],"Naive":[55],"Bayes,":[56],"$k$-means":[57],"clustering,":[58],"classification":[59],"regression":[61],"trees,":[62],"simulated":[63],"annealing,":[64],"local":[65],"search,":[66],"active":[67,123],"learning,":[68],"complementary-Bayes":[70],"text-mining":[71,162],"relevance":[72],"filtering":[73],"for":[74],"SE":[76,135,199],"data.":[77],"EZR":[78,220],"was":[79],"built":[80],"by":[81],"repeatedly":[82],"reading":[83,201],"refactoring":[85,203],"AI":[86],"tools":[87,144,154],"simplify":[89],"unify":[91],"them.":[92],"The":[93],"result":[94],"demonstrates":[95],"many":[97],"seemingly":[98],"different":[99],"learning":[100],"algorithms":[101,113],"nearly":[103],"same":[105],"once":[106],"stripped":[107],"back":[108],"their":[110],"core:":[111],"classical":[112],"collapse":[114],"few":[117],"each,":[119],"state-of-the-art":[122,152],"learner":[124],"fits":[125],"in":[126,138],"roughly":[127],"80":[128],"lines.":[129],"Tested":[130],"on":[131],"120+":[133],"tasks":[137],"MOOT":[140],"repository,":[141],"these":[142],"tiny":[143],"perform":[145],"as":[146,148],"well":[147],"or":[149],"better":[150],"than":[151,169,183],"explanation":[153],"(SHAP,":[155],"LIME),":[156],"SMAC3":[158],"optimizer,":[159],"SVM-based":[161],"filters":[163],"(FASTREAD),":[164],"while":[165],"running":[166],"500$\\times$":[167],"faster":[168],"SMAC3,":[170],"using":[171],"orders":[172],"magnitude":[174],"less":[175],"labelled":[176],"data,":[177],"building":[179],"trees":[180],"from":[181],"fewer":[182],"ten":[184],"variables":[185],"even":[186],"when":[187],"thousands":[188],"available.":[190],"conclude":[192],"that,":[193],"scope":[196],"optimization,":[200],"code":[204],"is":[205,221],"useful":[207],"method":[208],"generating":[210],"insight,":[211],"small":[213],"unified":[214],"toolkits":[215],"can":[216],"rival":[217],"large":[218],"libraries.":[219],"available":[222],"under":[223],"an":[224],"open-source":[225],"license.":[226],"Install":[227],"via":[228],"\\textsf{pip":[229],"install":[230],"ezr};":[231],"example":[232],"data":[233],"\\textsf{github.com/timm/moot}.":[235]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}
