{"id":"https://openalex.org/W1973434865","doi":"https://doi.org/10.1109/esem.2009.5315991","title":"Applying support vector regression for web effort estimation using a cross-company dataset","display_name":"Applying support vector regression for web effort estimation using a cross-company dataset","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W1973434865","doi":"https://doi.org/10.1109/esem.2009.5315991","mag":"1973434865"},"language":"en","primary_location":{"id":"doi:10.1109/esem.2009.5315991","is_oa":false,"landing_page_url":"https://doi.org/10.1109/esem.2009.5315991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 3rd International Symposium on Empirical Software Engineering and Measurement","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/A5064332237","display_name":"Anna Corazza","orcid":"https://orcid.org/0000-0002-9156-5079"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"A. Corazza","raw_affiliation_strings":["University of Napoli Federico II, Napoli, Italy","University of Napoli \u00bfFederico II\u00bf, Via Cinthia, I-80126, Napoli, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Napoli Federico II, Napoli, Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Napoli \u00bfFederico II\u00bf, Via Cinthia, I-80126, Napoli, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106313970","display_name":"Sergio Di Martino","orcid":"https://orcid.org/0000-0002-1019-9004"},"institutions":[{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"S. Di Martino","raw_affiliation_strings":["University of Napoli Federico II, Napoli, Italy","University of Napoli \u00bfFederico II\u00bf, Via Cinthia, I-80126, Napoli, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Napoli Federico II, Napoli, Italy","institution_ids":["https://openalex.org/I71267560"]},{"raw_affiliation_string":"University of Napoli \u00bfFederico II\u00bf, Via Cinthia, I-80126, Napoli, Italy","institution_ids":["https://openalex.org/I71267560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053084752","display_name":"Filomena Ferrucci","orcid":"https://orcid.org/0000-0002-0975-8972"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"F. Ferrucci","raw_affiliation_strings":["University of Salerno, Fisciano, Salerno, Italy","University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA), Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Salerno, Fisciano, Salerno, Italy","institution_ids":["https://openalex.org/I131729948"]},{"raw_affiliation_string":"University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA), Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012888719","display_name":"Carmine Gravino","orcid":"https://orcid.org/0000-0002-4394-9035"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"C. Gravino","raw_affiliation_strings":["University of Salerno, Fisciano, Salerno, Italy","University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA), Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Salerno, Fisciano, Salerno, Italy","institution_ids":["https://openalex.org/I131729948"]},{"raw_affiliation_string":"University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA), Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085558039","display_name":"E. Mendes","orcid":"https://orcid.org/0000-0002-3859-3548"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"E. Mendes","raw_affiliation_strings":["University of Auckland, Auckland, New Zealand","The University of Auckland, Private Bag 92019, Auckland (New Zealand)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]},{"raw_affiliation_string":"The University of Auckland, Private Bag 92019, Auckland (New Zealand)","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.435,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.95713886,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"24","issue":null,"first_page":"191","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9994999766349792,"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":0.9994999766349792,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9919000267982483,"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/T11122","display_name":"Online Learning and Analytics","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/support-vector-machine","display_name":"Support vector machine","score":0.7023262977600098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6469369530677795},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5295584797859192},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.508274257183075},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.47998496890068054},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47048547863960266},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.45308980345726013},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4515754282474518},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.44399896264076233},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4321553707122803},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.4184648394584656},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.267817884683609},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25527238845825195}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7023262977600098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469369530677795},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5295584797859192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508274257183075},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.47998496890068054},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47048547863960266},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.45308980345726013},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4515754282474518},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.44399896264076233},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4321553707122803},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.4184648394584656},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.267817884683609},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25527238845825195},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/esem.2009.5315991","is_oa":false,"landing_page_url":"https://doi.org/10.1109/esem.2009.5315991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 3rd International Symposium on Empirical Software Engineering and Measurement","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W56743589","https://openalex.org/W123442453","https://openalex.org/W165207608","https://openalex.org/W409166618","https://openalex.org/W854322902","https://openalex.org/W1522987989","https://openalex.org/W1530489096","https://openalex.org/W1575961892","https://openalex.org/W1576520375","https://openalex.org/W1594031697","https://openalex.org/W1964357740","https://openalex.org/W1986033379","https://openalex.org/W1987979080","https://openalex.org/W1997406557","https://openalex.org/W2003896859","https://openalex.org/W2009332873","https://openalex.org/W2023479632","https://openalex.org/W2024284172","https://openalex.org/W2069756886","https://openalex.org/W2100164091","https://openalex.org/W2102899008","https://openalex.org/W2104789242","https://openalex.org/W2104980832","https://openalex.org/W2106282576","https://openalex.org/W2113199992","https://openalex.org/W2118115183","https://openalex.org/W2124455919","https://openalex.org/W2125231355","https://openalex.org/W2125791732","https://openalex.org/W2129707602","https://openalex.org/W2135452472","https://openalex.org/W2137552337","https://openalex.org/W2148603752","https://openalex.org/W2154732019","https://openalex.org/W2156909104","https://openalex.org/W2159152612","https://openalex.org/W2169537402","https://openalex.org/W3023786531","https://openalex.org/W3085162807","https://openalex.org/W4233153129","https://openalex.org/W4237213195","https://openalex.org/W4239392469","https://openalex.org/W4241607863","https://openalex.org/W6602288617","https://openalex.org/W6604961309","https://openalex.org/W6634442568"],"related_works":["https://openalex.org/W3195168932","https://openalex.org/W1996541855","https://openalex.org/W2081545345","https://openalex.org/W3217770377","https://openalex.org/W3106646795","https://openalex.org/W4225835446","https://openalex.org/W2121418843","https://openalex.org/W2008757383","https://openalex.org/W3215147392","https://openalex.org/W4366829006"],"abstract_inverted_index":{"Support":[0],"vector":[1],"regression":[2],"(SVR)":[3],"is":[4,23,109],"a":[5,39,44,87,188],"new":[6],"generation":[7],"of":[8,20,28,191],"machine":[9],"learning":[10],"algorithms,":[11],"suitable":[12],"for":[13,30,59,96,100,127,204,220],"predictive":[14],"data":[15,137],"modeling":[16],"problems.":[17],"The":[18],"objective":[19],"this":[21],"paper":[22],"to":[24,122,160],"investigate":[25],"the":[26,48,101,105,124,129,149,181,184,200,205,208],"effectiveness":[27],"SVR":[29,89,176],"Web":[31,113],"effort":[32,114],"estimation,":[33],"in":[34,112],"particular":[35],"when":[36],"dealing":[37],"with":[38,187],"cross-company":[40],"dataset.":[41],"To":[42],"gain":[43],"deeper":[45],"insight":[46],"on":[47,138,180],"method,":[49],"we":[50,68,103],"carried":[51],"out":[52],"an":[53],"empirical":[54],"study":[55],"using":[56,92,131,163],"four":[57],"kernels":[58],"SVR,":[60],"namely":[61],"linear,":[62],"polynomial,":[63],"Gaussian,":[64],"and":[65,75,77,83,143,169],"sigmoid.":[66],"Moreover,":[67],"used":[69],"two":[70,78,132,144],"variables'":[71],"preprocessing":[72],"strategies":[73],"(normalization":[74],"logarithmic),":[76],"different":[79,94],"dependent":[80],"variables":[81,192],"(effort":[82],"inverse":[84],"effort).":[85],"As":[86,99,153],"result,":[88],"was":[90,120],"applied":[91],"six":[93],"configurations":[95],"each":[97,135,147],"kernel.":[98],"dataset,":[102],"employed":[104],"Tukutuku":[106],"database,":[107],"which":[108],"widely":[110],"adopted":[111,121],"estimation":[115],"studies.":[116],"A":[117],"hold-out":[118],"approach":[119],"evaluate":[123],"prediction":[125,196],"accuracy":[126,197],"all":[128,199,216],"configurations,":[130],"training":[133],"sets,":[134,146],"containing":[136,148],"130":[139],"projects":[140],"randomly":[141],"selected,":[142],"test":[145],"remaining":[150],"65":[151],"projects.":[152],"benchmark,":[154],"SVR-based":[155],"predictions":[156,161,214],"were":[157],"also":[158],"compared":[159],"obtained":[162],"manual":[164,221],"stepwise":[165,222],"regression,":[166],"case-based":[167],"reasoning,":[168],"Bayesian":[170],"networks.":[171],"Our":[172],"results":[173],"suggest":[174],"that":[175],"performed":[177],"well,":[178],"since":[179],"first":[182],"hold-out,":[183,207],"linear":[185],"kernel":[186,210],"logarithmic":[189],"transformation":[190],"provided":[193],"significantly":[194,212],"superior":[195,213],"than":[198,215],"other":[201,217],"techniques,":[202,218],"while":[203],"second":[206],"Gaussian":[209],"achieved":[211],"except":[219],"regression.":[223]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
