{"id":"https://openalex.org/W7153981744","doi":"https://doi.org/10.1007/s10586-026-05965-6","title":"Investigating the effectiveness of abstract syntax tree for refactoring prediction at class level using LSTM","display_name":"Investigating the effectiveness of abstract syntax tree for refactoring prediction at class level using LSTM","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7153981744","doi":"https://doi.org/10.1007/s10586-026-05965-6"},"language":"en","primary_location":{"id":"doi:10.1007/s10586-026-05965-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10586-026-05965-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10586-026-05965-6.pdf","source":{"id":"https://openalex.org/S106148199","display_name":"Cluster Computing","issn_l":"1386-7857","issn":["1386-7857","1573-7543"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cluster Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10586-026-05965-6.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024754760","display_name":"Rasmita Panigrahi","orcid":"https://orcid.org/0000-0003-3980-7306"},"institutions":[{"id":"https://openalex.org/I4210139271","display_name":"GIET University","ror":"https://ror.org/051f2wp73","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210139271"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rasmita Panigrahi","raw_affiliation_strings":["GIET University, Gunupur, India"],"affiliations":[{"raw_affiliation_string":"GIET University, Gunupur, India","institution_ids":["https://openalex.org/I4210139271"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133528830","display_name":"Sanjay Misra","orcid":null},"institutions":[{"id":"https://openalex.org/I3130438513","display_name":"Institute for Energy Technology","ror":"https://ror.org/02jqtg033","country_code":"NO","type":"facility","lineage":["https://openalex.org/I3130438513"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Sanjay Misra","raw_affiliation_strings":["Institute for Energy Technology, Halden, Norway"],"affiliations":[{"raw_affiliation_string":"Institute for Energy Technology, Halden, Norway","institution_ids":["https://openalex.org/I3130438513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075237293","display_name":"Lov Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I105094715","display_name":"National Institute of Technology Kurukshetra","ror":"https://ror.org/04909p852","country_code":"IN","type":"education","lineage":["https://openalex.org/I105094715"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lov Kumar","raw_affiliation_strings":["National Institute of Technology, Kurukshetra, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology, Kurukshetra, India","institution_ids":["https://openalex.org/I105094715"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5133528830"],"corresponding_institution_ids":["https://openalex.org/I3130438513"],"apc_list":{"value":2190,"currency":"EUR","value_usd":2790},"apc_paid":{"value":2190,"currency":"EUR","value_usd":2790},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.95077614,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9846000075340271,"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":0.9846000075340271,"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/T10430","display_name":"Software Engineering Techniques and Practices","score":0.0015999999595806003,"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":0.0010000000474974513,"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":0.977400004863739},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.7520999908447266},{"id":"https://openalex.org/keywords/abstract-syntax-tree","display_name":"Abstract syntax tree","score":0.6654999852180481},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5687000155448914},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5447999835014343},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.539900004863739},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5278000235557556},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.48559999465942383},{"id":"https://openalex.org/keywords/abstract-syntax","display_name":"Abstract syntax","score":0.48260000348091125}],"concepts":[{"id":"https://openalex.org/C152752567","wikidata":"https://www.wikidata.org/wiki/Q116877","display_name":"Code refactoring","level":3,"score":0.977400004863739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8995000123977661},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.7520999908447266},{"id":"https://openalex.org/C58646249","wikidata":"https://www.wikidata.org/wiki/Q127380","display_name":"Abstract syntax tree","level":3,"score":0.6654999852180481},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5687000155448914},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5447999835014343},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4993000030517578},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C114408938","wikidata":"https://www.wikidata.org/wiki/Q333373","display_name":"Abstract syntax","level":3,"score":0.48260000348091125},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42329999804496765},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37610000371932983},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.3605000078678131},{"id":"https://openalex.org/C82214349","wikidata":"https://www.wikidata.org/wiki/Q657339","display_name":"Software metric","level":5,"score":0.3188000023365021},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.31189998984336853},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C548217200","wikidata":"https://www.wikidata.org/wiki/Q251","display_name":"Java","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C149091818","wikidata":"https://www.wikidata.org/wiki/Q2429814","display_name":"Software system","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10586-026-05965-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10586-026-05965-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10586-026-05965-6.pdf","source":{"id":"https://openalex.org/S106148199","display_name":"Cluster Computing","issn_l":"1386-7857","issn":["1386-7857","1573-7543"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cluster Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10586-026-05965-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10586-026-05965-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10586-026-05965-6.pdf","source":{"id":"https://openalex.org/S106148199","display_name":"Cluster Computing","issn_l":"1386-7857","issn":["1386-7857","1573-7543"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cluster Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5874029994010925,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7153981744.pdf","grobid_xml":"https://content.openalex.org/works/W7153981744.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W1524601730","https://openalex.org/W2066950258","https://openalex.org/W2367248328","https://openalex.org/W2406602165","https://openalex.org/W2511803001","https://openalex.org/W2589119856","https://openalex.org/W2606020393","https://openalex.org/W2741808402","https://openalex.org/W2754638064","https://openalex.org/W2766296277","https://openalex.org/W2791390530","https://openalex.org/W2795027827","https://openalex.org/W2809192110","https://openalex.org/W2811418501","https://openalex.org/W2888711832","https://openalex.org/W2894923858","https://openalex.org/W2912148900","https://openalex.org/W2938071025","https://openalex.org/W2944080842","https://openalex.org/W2964150020","https://openalex.org/W2965531700","https://openalex.org/W2979271470","https://openalex.org/W2991900792","https://openalex.org/W2996361803","https://openalex.org/W3000216919","https://openalex.org/W3010946587","https://openalex.org/W3038994920","https://openalex.org/W3039357523","https://openalex.org/W3040397944","https://openalex.org/W3046270433","https://openalex.org/W3087577597","https://openalex.org/W3087926510","https://openalex.org/W3097082872","https://openalex.org/W3100891549","https://openalex.org/W3133994035","https://openalex.org/W3135845056","https://openalex.org/W3161551626","https://openalex.org/W3161979618","https://openalex.org/W3173578636","https://openalex.org/W3175567922","https://openalex.org/W3175658129","https://openalex.org/W3178819629","https://openalex.org/W3191581589","https://openalex.org/W3198034625","https://openalex.org/W3201121511","https://openalex.org/W3203221575","https://openalex.org/W3208323417","https://openalex.org/W3216427897","https://openalex.org/W4200249747","https://openalex.org/W4200335393","https://openalex.org/W4205420848","https://openalex.org/W4220943478","https://openalex.org/W4281392290","https://openalex.org/W4281815921","https://openalex.org/W4285260260","https://openalex.org/W4285337522","https://openalex.org/W4310713647","https://openalex.org/W4381198270","https://openalex.org/W4390861166"],"related_works":[],"abstract_inverted_index":{"Many":[0],"researchers":[1],"have":[2,105,134,188,207,389],"recommended":[3],"refactoring":[4,161,184,222],"models":[5,311],"through":[6,63,90,126,153],"source":[7,71,216],"code":[8,72,217],"metrics":[9,73,166,400],"based":[10],"on":[11,273,362],"a":[12,58,144,191,285,291,377],"threshold":[13,31,47],"value.":[14,32],"However,":[15],"this":[16],"approach":[17],"is":[18,35,80,101,119,338,373,406],"not":[19],"universally":[20],"acceptable":[21,46],"in":[22,149],"the":[23,76,91,107,114,154,158,169,172,180,183,229,246,255,281,326,331,339,348,392],"industry":[24],"because":[25],"each":[26,83,139,150],"organisation":[27],"has":[28],"its":[29],"own":[30],"Therefore,":[33],"it":[34,118,372],"desirable":[36],"to":[37,56,74,112,156,178,257,346],"develop":[38,57],"an":[39,45,64,271],"automated":[40],"model":[41,211],"that":[42,325,350,357,376,404],"can":[43],"determine":[44],"value":[48],"for":[49,82,138,385],"most":[50],"of":[51,146,160,182,277,288,294,335,380],"them.":[52],"This":[53],"paper":[54],"aims":[55],"Software":[59],"Refactoring":[60],"Model":[61],"(SRM)":[62],"abstract":[65],"syntax":[66],"tree":[67,128],"(AST)":[68],"rather":[69],"than":[70,365,410],"predict":[75,157,347],"class-level":[77],"refactoring.":[78,352,387],"AST":[79,214,397,405],"generated":[81],"data":[84,99,109],"set":[85,100],"(Antlr4,":[86],"Mct,":[87],"Titan,":[88],"Junit)":[89],"check":[92],"style":[93],"9.0.1":[94],"tool.":[95],"As":[96],"our":[97,209,220],"considered":[98,135],"purely":[102],"imbalanced,":[103],"we":[104,133],"used":[106,204],"SMOTE":[108,249],"sampling":[110],"technique":[111],"balance":[113],"data,":[115],"and":[116,176,200,215,236,290,398,402],"then":[117,164],"tokenized.":[120],"After":[121],"tokenization,":[122],"words":[123,137,148,279,329],"are":[124],"collected":[125],"different":[127,198],"traversal":[129],"techniques.":[130],"In":[131,219,296],"total,":[132],"500":[136],"project.":[140],"Then,":[141],"LSTM":[142,196,226,233],"takes":[143],"sequence":[145],"50":[147],"group,":[151],"incrementally":[152],"padding,":[155],"requirement":[159],"classes.":[162],"We":[163,187,206,388],"estimate":[165],"such":[167],"as":[168],"Area":[170],"under":[171],"Curve":[173],"(AUC),":[174],"F-score,":[175],"accuracy":[177,287],"measure":[179],"performance":[181,231],"prediction":[185],"model.":[186],"also":[189,269,355,374,390],"performed":[190],"comparative":[192,297],"analysis":[193],"by":[194,212],"applying":[195,396],"with":[197,201,248,284],"layers":[199,381],"other":[202,366],"frequently":[203],"classifiers.":[205],"evaluated":[208],"proposed":[210,221],"using":[213],"metrics.":[218],"model,":[223],"The":[224],"three-layer":[225],"(LSTM3)":[227],"had":[228,270],"best":[230],"among":[232,242,342],"architectures":[234],"(Accuracy=96.24%":[235],"AUC=0.58)":[237],"but":[238],"BNB":[239,358],"performs":[240,359],"well":[241],"all":[243,343],"(AUC=0.87).":[244],"Balancing":[245],"dataset":[247],"further":[250],"enhanced":[251],"discrimination":[252],"ability,":[253],"increasing":[254],"AUC":[256,293,318,333,363],"0.98":[258],"(median":[259],"=":[260,316,319],"1.00),":[261],"up":[262],"from":[263],"0.78":[264],"before":[265],"balancing.":[266],"Sequence":[267],"length":[268],"impact":[272],"performance:":[274],"shorter":[275],"inputs":[276],"150":[278,328],"produced":[280],"greatest":[282],"results,":[283],"mean":[286,292,332],"97.15%":[289],"0.61.":[295],"trials,":[298],"Bernoulli":[299],"Naive":[300],"Bayes":[301],"(BNB)":[302],"consistently":[303],"beat":[304],"traditional":[305],"classifiers,":[306,368],"including":[307,369],"LSTM,":[308],"while":[309],"AST-based":[310],"outperformed":[312],"object-oriented":[313,399],"measures":[314],"(accuracy":[315],"94.66%,":[317],"0.94).":[320],"Our":[321,353],"experimental":[322],"result":[323],"suggests":[324],"Initial":[327],"achieve":[330],"rank":[334],"4.47,":[336],"which":[337],"highest":[340],"performer":[341],"ten":[344],"groups":[345],"classes":[349],"need":[351],"results":[354,384,393,409],"show":[356],"better":[360,408],"(based":[361],"value)":[364],"well-known":[367],"LSTM.":[370],"Additionally,":[371],"observed":[375,403],"larger":[378],"number":[379],"obtains":[382],"significant":[383],"software":[386],"compared":[391],"obtained":[394],"after":[395],"(OOM)":[401],"obtaining":[407],"OOM.":[411]},"counts_by_year":[],"updated_date":"2026-04-15T05:59:14.812645","created_date":"2026-04-14T00:00:00"}
