{"id":"https://openalex.org/W4387185210","doi":"https://doi.org/10.3233/faia230317","title":"Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation","display_name":"Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation","publication_year":2023,"publication_date":"2023-09-28","ids":{"openalex":"https://openalex.org/W4387185210","doi":"https://doi.org/10.3233/faia230317"},"language":"en","primary_location":{"id":"doi:10.3233/faia230317","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia230317","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230317","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230317","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077542599","display_name":"Yihong Dong","orcid":"https://orcid.org/0000-0002-6048-2377"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihong Dong","raw_affiliation_strings":["Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076676389","display_name":"Ge Li","orcid":"https://orcid.org/0000-0003-4079-3968"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Li","raw_affiliation_strings":["Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074129836","display_name":"Xue Jiang","orcid":"https://orcid.org/0000-0001-7099-6817"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Jiang","raw_affiliation_strings":["Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049100391","display_name":"Zhi Jin","orcid":"https://orcid.org/0000-0003-1087-226X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Jin","raw_affiliation_strings":["Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of High Confidence Software Technology, MoE (Peking University), dongyh@stu.pku.edu.cn, lige@pku.edu.cn, jiangxue@stu.pku.edu.cn, zhijin@pku.edu.cn","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.4384,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83944806,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"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":0.9987999796867371,"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":0.9987999796867371,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9932000041007996,"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/T11450","display_name":"Model-Driven Software Engineering Techniques","score":0.9818999767303467,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/antecedent","display_name":"Antecedent (behavioral psychology)","score":0.9618935585021973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7478775382041931},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6443573236465454},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6207370162010193},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5990234017372131},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.521653950214386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4512098431587219},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.45055651664733887},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4172394871711731},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.41553935408592224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34862327575683594},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26662683486938477},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.2616598606109619},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1677582859992981},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09426522254943848},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08979633450508118},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.08782890439033508}],"concepts":[{"id":"https://openalex.org/C2781256819","wikidata":"https://www.wikidata.org/wiki/Q16828835","display_name":"Antecedent (behavioral psychology)","level":2,"score":0.9618935585021973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7478775382041931},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6443573236465454},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6207370162010193},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5990234017372131},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.521653950214386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4512098431587219},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.45055651664733887},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4172394871711731},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.41553935408592224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34862327575683594},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26662683486938477},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2616598606109619},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1677582859992981},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09426522254943848},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08979633450508118},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.08782890439033508},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138496976","wikidata":"https://www.wikidata.org/wiki/Q175002","display_name":"Developmental psychology","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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:10.3233/faia230317","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia230317","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230317","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia230317","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia230317","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230317","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387185210.pdf"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2077302143","https://openalex.org/W2163274265","https://openalex.org/W2224454470","https://openalex.org/W2304240348","https://openalex.org/W2605887895","https://openalex.org/W2610002206","https://openalex.org/W2751262944","https://openalex.org/W2804673050","https://openalex.org/W2889467844","https://openalex.org/W2890867094","https://openalex.org/W2896392119","https://openalex.org/W2901813505","https://openalex.org/W2949215742","https://openalex.org/W2955270045","https://openalex.org/W2962728167","https://openalex.org/W2963357517","https://openalex.org/W2963617989","https://openalex.org/W2963794306","https://openalex.org/W2963868406","https://openalex.org/W2964315653","https://openalex.org/W2964325845","https://openalex.org/W2971008324","https://openalex.org/W2979486033","https://openalex.org/W2990448925","https://openalex.org/W2997847174","https://openalex.org/W3017094847","https://openalex.org/W3034976548","https://openalex.org/W3114589574","https://openalex.org/W3121707215","https://openalex.org/W3126675481","https://openalex.org/W3165081941","https://openalex.org/W3171200957","https://openalex.org/W3172275282","https://openalex.org/W3176868790","https://openalex.org/W3198685994","https://openalex.org/W4205749957","https://openalex.org/W4220722393","https://openalex.org/W4226075195","https://openalex.org/W4293255850","https://openalex.org/W4306317317","https://openalex.org/W4307932749","https://openalex.org/W4308648312","https://openalex.org/W4313936359","https://openalex.org/W4317940234","https://openalex.org/W4319792740","https://openalex.org/W4366342667","https://openalex.org/W4375957731","https://openalex.org/W4384155659","https://openalex.org/W4384159055","https://openalex.org/W4384302891","https://openalex.org/W4385245566","https://openalex.org/W4385572345","https://openalex.org/W4387360102"],"related_works":["https://openalex.org/W1514236140","https://openalex.org/W2355247546","https://openalex.org/W2379488605","https://openalex.org/W1588921741","https://openalex.org/W3117285250","https://openalex.org/W2018225658","https://openalex.org/W27140714","https://openalex.org/W1913498823","https://openalex.org/W1529792186","https://openalex.org/W2151028259"],"abstract_inverted_index":{"Code":[0],"generation":[1,20,172],"focuses":[2],"on":[3,42,177],"automatically":[4],"converting":[5],"natural":[6],"language":[7],"(NL)":[8],"utterances":[9],"into":[10],"code":[11,19,171],"snippets.":[12],"Sequence-to-tree":[13],"(Seq2Tree)":[14],"approaches":[15,33,52],"are":[16,78],"proposed":[17,161,202],"for":[18,67],"with":[21,183],"the":[22,29,43,75,81,84,125,129,149,157,196],"aim":[23],"of":[24,28,46,83,128,152,159,200],"ensuring":[25],"grammatical":[26],"correctness":[27],"generated":[30,130],"code.":[31],"These":[32],"generate":[34],"subsequent":[35,60,72,101,189],"Abstract":[36],"Syntax":[37],"Tree":[38],"(AST)":[39],"nodes":[40],"based":[41],"preceding":[44],"predictions":[45,58,61,73,77,98,122,186],"AST":[47,131,141,153],"nodes.":[48,132,154],"However,":[49],"existing":[50],"Seq2Tree":[51],"tend":[53],"to":[54,69,92,96,100,144],"treat":[55],"both":[56],"antecedent":[57,76,97,121,185],"and":[59,111,165,187,198],"equally,":[62],"which":[63,119],"poses":[64],"a":[65,109],"challenge":[66],"models":[68],"produce":[70],"accurate":[71],"if":[74],"incorrect":[79],"under":[80],"constraints":[82],"AST.":[85],"Given":[86],"this":[87,104,106],"challenge,":[88],"it":[89],"is":[90],"necessary":[91],"pay":[93],"more":[94],"attention":[95],"compared":[99],"predictions.":[102],"To":[103,155],"end,":[105],"paper":[107],"proposes":[108],"novel":[110],"effective":[112],"method,":[113],"named":[114],"Antecedent":[115,168],"Prioritized":[116,169],"(AP)":[117],"Loss,":[118],"prioritizes":[120],"by":[123],"leveraging":[124],"position":[126,150],"information":[127,151],"We":[133],"design":[134],"an":[135,167],"AST-to-Vector":[136],"(AST2Vec)":[137],"method":[138],"that":[139,182],"maps":[140],"node":[142],"positions":[143],"two-dimensional":[145],"vectors,":[146],"thereby":[147],"modeling":[148],"evaluate":[156],"effectiveness":[158],"our":[160,201],"loss,":[162],"we":[163],"implement":[164],"train":[166],"Tree-based":[170],"model":[173],"called":[174],"APT.":[175],"Experiments":[176],"four":[178],"benchmark":[179],"datasets":[180],"demonstrate":[181],"better":[184],"accompanying":[188],"predictions,":[190],"APT":[191],"achieves":[192],"significant":[193],"improvements,":[194],"indicating":[195],"superiority":[197],"generality":[199],"method.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}