{"id":"https://openalex.org/W2066452975","doi":"https://doi.org/10.1145/2020390.2020396","title":"Software effort estimation based on optimized model tree","display_name":"Software effort estimation based on optimized model tree","publication_year":2011,"publication_date":"2011-09-20","ids":{"openalex":"https://openalex.org/W2066452975","doi":"https://doi.org/10.1145/2020390.2020396","mag":"2066452975"},"language":"en","primary_location":{"id":"doi:10.1145/2020390.2020396","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020390.2020396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Predictive Models in Software Engineering","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/A5015423824","display_name":"Mohammad Azzeh","orcid":"https://orcid.org/0000-0002-0323-6452"},"institutions":[{"id":"https://openalex.org/I146367977","display_name":"Applied Science Private University","ror":"https://ror.org/01ah6nb52","country_code":"JO","type":"education","lineage":["https://openalex.org/I146367977"]}],"countries":["JO"],"is_corresponding":true,"raw_author_name":"Mohammad Azzeh","raw_affiliation_strings":["Applied Science University, Amman, Jordan","Applied Science University, Amman-Jordan#TAB#"],"affiliations":[{"raw_affiliation_string":"Applied Science University, Amman, Jordan","institution_ids":["https://openalex.org/I146367977"]},{"raw_affiliation_string":"Applied Science University, Amman-Jordan#TAB#","institution_ids":["https://openalex.org/I146367977"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5015423824"],"corresponding_institution_ids":["https://openalex.org/I146367977"],"apc_list":null,"apc_paid":null,"fwci":4.4337,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.94618306,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":1.0,"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":1.0,"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/T12423","display_name":"Software Reliability and Analysis Research","score":0.9984999895095825,"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/T10430","display_name":"Software Engineering Techniques and Practices","score":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7720320224761963},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6995601654052734},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6070513725280762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5729010701179504},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5659908652305603},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5145336985588074},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5132867693901062},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5065876245498657},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.488562673330307},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44905710220336914},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4377286732196808},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3397987484931946},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10598021745681763},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10312652587890625}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7720320224761963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6995601654052734},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6070513725280762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5729010701179504},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5659908652305603},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5145336985588074},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5132867693901062},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5065876245498657},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.488562673330307},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44905710220336914},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4377286732196808},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3397987484931946},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10598021745681763},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10312652587890625},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2020390.2020396","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020390.2020396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Predictive Models in Software Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323382","display_name":"University of Jordan","ror":"https://ror.org/05k89ew48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W16088600","https://openalex.org/W168970045","https://openalex.org/W1572978214","https://openalex.org/W1585159768","https://openalex.org/W1925139028","https://openalex.org/W1965619387","https://openalex.org/W1989290647","https://openalex.org/W2013029322","https://openalex.org/W2022537368","https://openalex.org/W2032375313","https://openalex.org/W2092528416","https://openalex.org/W2094198523","https://openalex.org/W2106282576","https://openalex.org/W2109105289","https://openalex.org/W2109942136","https://openalex.org/W2112382199","https://openalex.org/W2125809971","https://openalex.org/W2127177630","https://openalex.org/W2131378644","https://openalex.org/W2144641356","https://openalex.org/W2157224061","https://openalex.org/W2159747233"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W2378211422","https://openalex.org/W4388145910","https://openalex.org/W2381570729","https://openalex.org/W1976205134","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W4248336175","https://openalex.org/W2031260042","https://openalex.org/W2130974462"],"abstract_inverted_index":{"Background:":[0],"It":[1],"is":[2,9,16,26,58],"widely":[3],"recognized":[4],"that":[5,25,101,215,236],"software":[6,29,125],"effort":[7,30,126,219,242],"estimation":[8,127,159,195,243],"a":[10,41,59,103],"regression":[11,23],"problem.":[12],"Model":[13],"Tree":[14],"(MT)":[15],"one":[17],"of":[18,44,55,74,83,98,157,184,210,224],"the":[19,38,72,81,85,95,120,154,208,213,225],"Machine":[20],"Learning":[21],"based":[22],"techniques":[24,214],"useful":[27],"for":[28,218,245],"estimation,":[31],"but":[32],"as":[33],"other":[34,193],"machine":[35],"learning":[36],"algorithms,":[37],"MT":[39,99,114,185,211,234],"has":[40,131],"large":[42],"space":[43],"configurations":[45],"and":[46,106,143,175,186,191],"requires":[47],"to":[48,93,123,151,167,206,230,239],"carefully":[49],"setting":[50],"its":[51],"parameters.":[52],"The":[53,129,179,222],"choice":[54,97],"such":[56],"parameters":[57,100,117,235],"dataset":[60,105],"dependent":[61],"so":[62],"no":[63],"general":[64],"guideline":[65],"can":[66],"govern":[67],"this":[68,75],"process":[69],"which":[70],"forms":[71],"motivation":[73],"work.":[76],"Aims:":[77],"This":[78],"study":[79],"investigates":[80],"effect":[82],"using":[84],"most":[86],"recent":[87],"optimization":[88],"algorithm":[89,92,122,188,227],"called":[90],"Bees":[91,121,187,226],"specify":[94],"optimal":[96,116,233],"fit":[102],"specific":[104],"therefore":[107],"improve":[108],"prediction":[109,155],"accuracy.":[110],"Method:":[111],"We":[112],"used":[113,147],"with":[115,170],"identified":[118],"by":[119],"construct":[124,240],"model.":[128],"model":[130,244],"been":[132],"validated":[133],"over":[134],"eight":[135],"datasets":[136],"come":[137],"from":[138,182],"two":[139],"main":[140],"sources:":[141],"PROMISE":[142],"ISBSG.":[144],"Also":[145],"we":[146],"3-Fold":[148],"cross":[149],"validation":[150],"empirically":[152],"assess":[153],"accuracies":[156],"different":[158],"models.":[160],"As":[161],"benchmark,":[162],"results":[163,180],"are":[164,189,202,216,237],"also":[165,203],"compared":[166],"those":[168],"obtained":[169,181],"Stepwise":[171],"Regression,":[172],"Case-Based":[173],"Reasoning":[174],"Multi-Layer":[176],"Perceptron.":[177],"Results:":[178],"combination":[183],"encouraging":[190],"outperforms":[192],"well-known":[194],"methods":[196],"applied":[197],"on":[198],"employed":[199],"datasets.":[200],"They":[201],"interesting":[204],"enough":[205],"suggest":[207],"effectiveness":[209],"among":[212],"suitable":[217],"estimation.":[220],"Conclusions:":[221],"use":[223],"enabled":[228],"us":[229],"automatically":[231],"find":[232],"required":[238],"accurate":[241],"each":[246],"individual":[247],"dataset.":[248]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
