{"id":"https://openalex.org/W4406459077","doi":"https://doi.org/10.1109/bigdata62323.2024.10825464","title":"Automated Synthesis of Distributed Code from Sequential Snippets Using Deep Learning","display_name":"Automated Synthesis of Distributed Code from Sequential Snippets Using Deep Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406459077","doi":"https://doi.org/10.1109/bigdata62323.2024.10825464"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5074517504","display_name":"Arun Sanjel","orcid":"https://orcid.org/0009-0009-2453-8538"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arun Sanjel","raw_affiliation_strings":["Baylor University,School of Engineering and Computer Science,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Baylor University,School of Engineering and Computer Science,Department of Computer Science","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063483595","display_name":"Bikram Khanal","orcid":"https://orcid.org/0000-0003-2292-520X"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bikram Khanal","raw_affiliation_strings":["Baylor University,School of Engineering and Computer Science,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Baylor University,School of Engineering and Computer Science,Department of Computer Science","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045360354","display_name":"Pablo Rivas","orcid":"https://orcid.org/0000-0002-8690-0987"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pablo Rivas","raw_affiliation_strings":["Baylor University,School of Engineering and Computer Science,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Baylor University,School of Engineering and Computer Science,Department of Computer Science","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112286821","display_name":"Greg Speegle","orcid":"https://orcid.org/0000-0003-1739-0271"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Greg Speegle","raw_affiliation_strings":["Baylor University,School of Engineering and Computer Science,Department of Computer Science"],"affiliations":[{"raw_affiliation_string":"Baylor University,School of Engineering and Computer Science,Department of Computer Science","institution_ids":["https://openalex.org/I157394403"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5074517504"],"corresponding_institution_ids":["https://openalex.org/I157394403"],"apc_list":null,"apc_paid":null,"fwci":0.4985,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72994769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4119","last_page":"4126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.9848999977111816,"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/computer-science","display_name":"Computer science","score":0.8304369449615479},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6131505966186523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5076931715011597},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4763760268688202},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.39204537868499756},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.34650719165802},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33818939328193665},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3355940878391266}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8304369449615479},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6131505966186523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5076931715011597},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4763760268688202},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.39204537868499756},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.34650719165802},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33818939328193665},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3355940878391266},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2038412523","https://openalex.org/W2119738171","https://openalex.org/W2173213060","https://openalex.org/W2189465200","https://openalex.org/W2785559831","https://openalex.org/W2786248241","https://openalex.org/W2954996726","https://openalex.org/W3022425873","https://openalex.org/W3098585900","https://openalex.org/W3109840491","https://openalex.org/W3174828871","https://openalex.org/W3176923149","https://openalex.org/W3198188208","https://openalex.org/W4235505822","https://openalex.org/W4294990530","https://openalex.org/W4386566536","https://openalex.org/W4388581326","https://openalex.org/W4389438938","https://openalex.org/W6687322159","https://openalex.org/W6747273815","https://openalex.org/W6748178589","https://openalex.org/W6755023921","https://openalex.org/W6786737052","https://openalex.org/W6792072638","https://openalex.org/W6794796237","https://openalex.org/W6801278399","https://openalex.org/W6810691633"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W2804383999","https://openalex.org/W2802049774"],"abstract_inverted_index":{"Processing":[0],"big":[1],"data":[2,22],"poses":[3],"a":[4,121,126,131,152,157,162],"significant":[5],"challenge":[6],"when":[7],"transitioning":[8],"from":[9,28],"sequential":[10,84,110],"to":[11,16,63,102],"distributed":[12,104],"code,":[13],"primarily":[14],"due":[15],"the":[17,35,54,96,113,116,144,176,180],"extensive":[18],"scale":[19],"at":[20],"which":[21],"is":[23],"handled.":[24],"This":[25],"complexity":[26],"arises":[27],"both":[29],"syntax":[30],"and":[31,42,65,82,136,167],"semantic":[32],"differences":[33],"between":[34],"codes.":[36],"Unfortunately,":[37],"current":[38],"methods":[39],"are":[40],"inefficient":[41],"require":[43],"more":[44],"effective":[45],"automated":[46],"solutions.":[47],"To":[48],"address":[49],"this":[50],"problem,":[51],"we":[52],"utilized":[53],"Transformer-based":[55],"BERT":[56,97],"model":[57,98,117],"because":[58],"of":[59,124,129,134,140,155,160,165,171,178],"its":[60],"exceptional":[61],"capability":[62],"understand":[64],"capture":[66],"deep":[67],"contextual":[68],"relationships":[69],"in":[70],"large":[71],"datasets.":[72],"Our":[73,173],"method":[74],"involved":[75],"creating":[76],"two":[77],"comprehensive":[78],"datasets":[79],"containing":[80],"10k":[81,114],"100k":[83,145],"code":[85,182],"snippets":[86],"paired":[87],"with":[88,151,184],"their":[89],"corresponding":[90],"PySpark":[91],"API":[92,105],"calls.":[93],"We":[94],"optimized":[95],"by":[99],"fine-tuning":[100],"it":[101],"predict":[103],"calls":[106],"for":[107],"previously":[108],"unseen":[109],"snippets.":[111],"For":[112],"dataset,":[115],"demonstrated":[118],"robustness,":[119],"achieving":[120],"training":[122,153],"accuracy":[123,128,154,159],"99.36%,":[125],"test":[127,158],"99.7%,":[130],"Balanced":[132,163],"Accuracy":[133,164],"99.78%,":[135],"an":[137,168],"F1":[138,169],"Score":[139,170],"0.997.":[141],"In":[142],"contrast,":[143],"dataset":[146],"metrics":[147],"were":[148],"equally":[149],"impressive,":[150],"89.48%,":[156],"99.99%,":[161],"99.98%,":[166],"0.999.":[172],"work":[174],"demonstrates":[175],"feasibility":[177],"automating":[179],"sequential-to-distributed":[181],"transition":[183],"notable":[185],"precision.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
