{"id":"https://openalex.org/W1966242183","doi":"https://doi.org/10.1145/2535838.2535853","title":"Bias-variance tradeoffs in program analysis","display_name":"Bias-variance tradeoffs in program analysis","publication_year":2014,"publication_date":"2014-01-08","ids":{"openalex":"https://openalex.org/W1966242183","doi":"https://doi.org/10.1145/2535838.2535853","mag":"1966242183"},"language":"en","primary_location":{"id":"doi:10.1145/2535838.2535853","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2535838.2535853","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages","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/A5101769895","display_name":"Rahul Sharma","orcid":"https://orcid.org/0000-0001-7527-4653"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rahul Sharma","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111937381","display_name":"Aditya V. Nori","orcid":null},"institutions":[{"id":"https://openalex.org/I4210124949","display_name":"Microsoft Research (India)","ror":"https://ror.org/02w7f3w92","country_code":"IN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210124949"]},{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["IN","US"],"is_corresponding":false,"raw_author_name":"Aditya V. Nori","raw_affiliation_strings":["Microsoft Research, Bangalore, India","Microsoft Research, Bangalore, India#TAB#"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Bangalore, India","institution_ids":["https://openalex.org/I4210124949"]},{"raw_affiliation_string":"Microsoft Research, Bangalore, India#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087402475","display_name":"Alex Aiken","orcid":"https://orcid.org/0000-0002-3723-9555"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Aiken","raw_affiliation_strings":["Stanford University, Stanford, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101769895"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":2.96,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.91841083,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"127","last_page":"137"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9983999729156494,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9983999729156494,"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":0.9902999997138977,"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/T10126","display_name":"Logic, programming, and type systems","score":0.9815000295639038,"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/variance","display_name":"Variance (accounting)","score":0.7717181444168091},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7536013126373291},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.701813280582428},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6641041040420532},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.426043838262558},{"id":"https://openalex.org/keywords/program-analysis","display_name":"Program analysis","score":0.41014426946640015},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3543972969055176},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3219417929649353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14978355169296265},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08784449100494385}],"concepts":[{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.7717181444168091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7536013126373291},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.701813280582428},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6641041040420532},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.426043838262558},{"id":"https://openalex.org/C98183937","wikidata":"https://www.wikidata.org/wiki/Q2112188","display_name":"Program analysis","level":2,"score":0.41014426946640015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3543972969055176},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3219417929649353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14978355169296265},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08784449100494385},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2535838.2535853","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2535838.2535853","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.734.3174","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.734.3174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://theory.stanford.edu/%7Eaiken/publications/papers/popl14.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4399999976158142,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W60370665","https://openalex.org/W70032344","https://openalex.org/W74317754","https://openalex.org/W1486658274","https://openalex.org/W1492171827","https://openalex.org/W1506806321","https://openalex.org/W1507199453","https://openalex.org/W1520252399","https://openalex.org/W1524963041","https://openalex.org/W1531054827","https://openalex.org/W1540154185","https://openalex.org/W1559488388","https://openalex.org/W1563374593","https://openalex.org/W1568478782","https://openalex.org/W1570919259","https://openalex.org/W1592616558","https://openalex.org/W1593025666","https://openalex.org/W1938285999","https://openalex.org/W1970357325","https://openalex.org/W1981552604","https://openalex.org/W1986269585","https://openalex.org/W2018756497","https://openalex.org/W2019363670","https://openalex.org/W2043100293","https://openalex.org/W2043876870","https://openalex.org/W2058869529","https://openalex.org/W2076118331","https://openalex.org/W2076409494","https://openalex.org/W2076719323","https://openalex.org/W2084294613","https://openalex.org/W2098022036","https://openalex.org/W2110503149","https://openalex.org/W2132661148","https://openalex.org/W2134875273","https://openalex.org/W2136535855","https://openalex.org/W2137628566","https://openalex.org/W2138263042","https://openalex.org/W2149647957","https://openalex.org/W2151463894","https://openalex.org/W2154776925","https://openalex.org/W2154952480","https://openalex.org/W2154985136","https://openalex.org/W2158395308","https://openalex.org/W2161336914","https://openalex.org/W2165086189","https://openalex.org/W2280703106","https://openalex.org/W3029115295","https://openalex.org/W3104887532","https://openalex.org/W3158747708","https://openalex.org/W4212935551","https://openalex.org/W4238893454","https://openalex.org/W4252599801","https://openalex.org/W4254377784"],"related_works":["https://openalex.org/W4361804730","https://openalex.org/W2142113611","https://openalex.org/W2334467465","https://openalex.org/W2087870008","https://openalex.org/W2162534555","https://openalex.org/W2752178021","https://openalex.org/W2107419853","https://openalex.org/W2143024819","https://openalex.org/W4247159817","https://openalex.org/W2964201926"],"abstract_inverted_index":{"It":[0,17],"is":[1,18,24],"often":[2],"the":[3,7,25,31,39,92,97,153],"case":[4],"that":[5,21,49],"increasing":[6],"precision":[8,86,110],"of":[9,27,45],"a":[10,88,109,119],"program":[11,113,129,167],"analysis":[12],"leads":[13],"to":[14,33,60,70],"worse":[15],"results.":[16],"our":[19],"thesis":[20],"this":[22],"phenomenon":[23],"result":[26],"fundamental":[28],"limits":[29],"on":[30,105,125],"ability":[32],"use":[34],"precise":[35,64],"abstract":[36],"domains":[37],"as":[38,108],"basis":[40],"for":[41,78,100,112,121,162],"inferring":[42],"strong":[43],"invariants":[44],"programs.":[46],"We":[47,95,115],"show":[48],"bias-variance":[50,123,164],"tradeoffs,":[51,124],"an":[52,126],"idea":[53],"from":[54],"learning":[55],"theory,":[56],"can":[57],"be":[58],"used":[59],"explain":[61],"why":[62],"more":[63],"abstractions":[65,102],"do":[66],"not":[67],"necessarily":[68],"lead":[69],"better":[71,142],"results":[72],"and":[73,103,148],"also":[74],"provides":[75],"practical":[76],"techniques":[77],"coping":[79],"with":[80],"such":[81],"limitations.":[82],"Learning":[83],"theory":[84],"captures":[85],"using":[87,137],"combinatorial":[89],"quantity":[90],"called":[91,132],"VC":[93,98],"dimension.":[94],"compute":[96],"dimension":[99],"different":[101],"report":[104],"its":[106],"usefulness":[107],"metric":[111],"analyses.":[114],"evaluate":[116],"cross":[117,138],"validation,":[118],"technique":[120],"addressing":[122],"industrial":[127],"strength":[128],"verification":[130],"tool":[131,135],"YOGI.":[133],"The":[134],"produced":[136],"validation":[139],"has":[140,149],"significantly":[141],"running":[143],"time,":[144],"finds":[145],"new":[146],"defects,":[147],"fewer":[150],"time-outs":[151],"than":[152],"current":[154],"production":[155],"version.":[156],"Finally,":[157],"we":[158],"make":[159],"some":[160],"recommendations":[161],"tackling":[163],"tradeoffs":[165],"in":[166],"analysis.":[168]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}