{"id":"https://openalex.org/W7138938699","doi":"https://doi.org/10.48550/arxiv.2603.17821","title":"CodeT5-RNN: Reinforcing Contextual Embeddings for Enhanced Code Comprehension","display_name":"CodeT5-RNN: Reinforcing Contextual Embeddings for Enhanced Code Comprehension","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7138938699","doi":"https://doi.org/10.48550/arxiv.2603.17821"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17821","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.17821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023926503","display_name":"Md. Mostafizer Rahman","orcid":"https://orcid.org/0000-0001-9368-7638"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rahman, Md Mostafizer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094247830","display_name":"Ariful Islam Shiplu","orcid":"https://orcid.org/0009-0009-4198-9023"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiplu, Ariful Islam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026370198","display_name":"Yutaka Watanobe","orcid":"https://orcid.org/0000-0002-0030-3859"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watanobe, Yutaka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061975231","display_name":"Md Faizul Ibne Amin","orcid":"https://orcid.org/0009-0001-0722-3536"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amin, Md Faizul Ibne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029966755","display_name":"Syed Rameez Naqvi","orcid":"https://orcid.org/0000-0001-6954-926X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naqvi, Syed Rameez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129780865","display_name":"Fang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5023926503"],"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/T10260","display_name":"Software Engineering Research","score":0.9200999736785889,"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":0.9200999736785889,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.03269999846816063,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.006599999964237213,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7168999910354614},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5404000282287598},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5059999823570251},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4611999988555908},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4474000036716461},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.43050000071525574},{"id":"https://openalex.org/keywords/macro","display_name":"Macro","score":0.4171999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480000257492065},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7168999910354614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5461999773979187},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5404000282287598},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43650001287460327},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3450999855995178},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3188999891281128},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C3018397939","wikidata":"https://www.wikidata.org/wiki/Q3644502","display_name":"Open source","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17821","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.17821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17821","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Contextual":[0],"embeddings":[1,34,58,65,198],"generated":[2],"by":[3,153],"LLMs":[4],"exhibit":[5],"strong":[6],"positional":[7],"inductive":[8],"biases,":[9],"which":[10],"can":[11],"limit":[12],"their":[13,145],"ability":[14],"to":[15,28,69],"fully":[16],"capture":[17],"long-range,":[18],"order-sensitive":[19],"dependencies":[20,76],"in":[21,78,206],"highly":[22],"structured":[23],"source":[24,79],"code.":[25,80],"Consequently,":[26],"how":[27],"further":[29,181],"refine":[30],"and":[31,73,90,101,108,122,128,134,141,148,151,163,168,184],"enhance":[32],"LLM":[33],"for":[35],"improved":[36],"code":[37,203],"understanding":[38,204],"remains":[39],"an":[40],"open":[41],"research":[42],"question.":[43],"To":[44],"address":[45],"this":[46],"gap,":[47],"we":[48],"propose":[49],"a":[50,60,154],"hybrid":[51,85],"LLM-RNN":[52],"framework":[53],"that":[54,98,195],"reinforces":[55],"LLM-generated":[56],"contextual":[57,197],"with":[59,199],"sequential":[61,71],"RNN":[62,200],"architecture.":[63],"The":[64,94],"reprocessing":[66,196],"step":[67],"aims":[68],"reinforce":[70],"semantics":[72],"strengthen":[74],"order-aware":[75],"inherent":[77],"We":[81],"evaluate":[82],"the":[83,99,112,125,132,172],"proposed":[84],"models":[86,103,136,147],"on":[87,111,171],"both":[88],"benchmark":[89,115],"real-world":[91,179],"coding":[92],"datasets.":[93],"experimental":[95],"results":[96],"show":[97],"RoBERTa-BiGRU":[100,150],"CodeBERT-GRU":[102,152],"achieved":[104,137],"accuracies":[105,138],"of":[106,119,139,166],"66.40%":[107],"66.03%,":[109],"respectively,":[110,143,170],"defect":[113],"detection":[114],"dataset,":[116],"representing":[117],"improvements":[118,187],"approximately":[120],"5.35%":[121],"3.95%":[123],"over":[124,188],"standalone":[126,189],"RoBERTa":[127],"CodeBERT":[129],"models.":[130,208],"Furthermore,":[131],"CodeT5-GRU":[133,159],"CodeT5+-BiGRU":[135],"67.90%":[140],"67.79%,":[142],"surpassing":[144],"base":[146],"outperforming":[149],"notable":[155],"margin.":[156],"In":[157],"addition,":[158],"model":[160],"attains":[161],"weighted":[162],"macro":[164],"F1-scores":[165],"67.18%":[167],"67.00%,":[169],"same":[173],"dataset.":[174],"Extensive":[175],"experiments":[176],"across":[177],"three":[178],"datasets":[180],"demonstrate":[182],"consistent":[183],"statistically":[185],"significant":[186],"LLMs.":[190],"Overall,":[191],"our":[192],"findings":[193],"indicate":[194],"architectures":[201],"enhances":[202],"performance":[205],"LLM-based":[207]},"counts_by_year":[],"updated_date":"2026-04-30T09:15:22.047038","created_date":"2026-03-20T00:00:00"}
