{"url":"/sota/code-generation-on-django","task":{"name":"Code Generation","url":"/task/code-generation","note":null},"dataset":{"name":"Django","url":"/dataset/django"},"category":"Natural Language Processing","categories":["Computer Code","Natural Language Processing","Reasoning"],"category_note":null,"description":"**Code Generation** is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.\r\n\r\n\r\n<span class=\"description-source\">Source: [Deep Learning for Source Code Modeling and Generation ](https://arxiv.org/abs/2002.05442)</span>\r\n\r\nImage source: [Measuring Coding Challenge Competence With APPS](https://paperswithcode.com/paper/measuring-coding-challenge-competence-with)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy","BLEU Score"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","BLEU Score":"higher"}},"counts":{"rows":11,"rows_with_code":10,"rows_with_paper_page":11,"rows_dated":11,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MarianCG","metrics":{"Accuracy":"81.83","BLEU Score":"90.41"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/mariancg-a-code-generation-transformer-model","paper_url":"https://jeas.springeropen.com/articles/10.1186/s44147-022-00159-4","paper_title":"MarianCG: a code generation transformer model inspired by machine translation","code":"https://github.com/AhmedSSoliman/MarianCG-NL-to-Code","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"TranX + BERT w/mined","metrics":{"Accuracy":"81.03","BLEU Score":"79.86"},"uses_additional_data":false,"paper_date":"2022-02-28","paper":"/paper/the-impact-of-lexical-and-grammatical-1","paper_url":"https://arxiv.org/abs/2202.13972v2","paper_title":"The impact of lexical and grammatical processing on generating code from natural language","code":"https://gitlab.com/codegenfactors/BertranX","n_code_links":2,"syntology":null},{"rank_in_archive_order":3,"model":"BERT + TAE","metrics":{"Accuracy":"81.03"},"uses_additional_data":false,"paper_date":"2021-01-01","paper":"/paper/semantic-parsing-with-less-prior-and-more","paper_url":"https://arxiv.org/abs/2101.00259v2","paper_title":"Code Generation from Natural Language with Less Prior and More Monolingual Data","code":"https://github.com/borealisai/code-gen-tae","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Reranker","metrics":{"Accuracy":"80.2"},"uses_additional_data":false,"paper_date":"2019-07-01","paper":"/paper/reranking-for-neural-semantic-parsing","paper_url":"https://aclanthology.org/P19-1447","paper_title":"Reranking for Neural Semantic Parsing","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"LUKEMarian","metrics":{"Accuracy":"78.50","BLEU Score":"89.34"},"uses_additional_data":false,"paper_date":"2024-02-29","paper":"/paper/leveraging-pre-trained-language-models-for-3","paper_url":"https://doi.org/10.1007/s40747-024-01373-8","paper_title":"Leveraging pre-trained language models for code generation","code":"https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"RoBERTaMarian","metrics":{"Accuracy":"77.95","BLEU Score":"88.91"},"uses_additional_data":false,"paper_date":"2024-02-29","paper":"/paper/leveraging-pre-trained-language-models-for-3","paper_url":"https://doi.org/10.1007/s40747-024-01373-8","paper_title":"Leveraging pre-trained language models for code generation","code":"https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"BERTMarian","metrics":{"Accuracy":"76.68","BLEU Score":"56.55"},"uses_additional_data":false,"paper_date":"2024-02-29","paper":"/paper/leveraging-pre-trained-language-models-for-3","paper_url":"https://doi.org/10.1007/s40747-024-01373-8","paper_title":"Leveraging pre-trained language models for code generation","code":"https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"Tranx","metrics":{"Accuracy":"73.7"},"uses_additional_data":false,"paper_date":"2018-10-05","paper":"/paper/tranx-a-transition-based-neural-abstract","paper_url":"http://arxiv.org/abs/1810.02720v1","paper_title":"TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation","code":"https://github.com/pcyin/tranX","n_code_links":4,"syntology":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":2}},{"rank_in_archive_order":9,"model":"ELECTRAMarian","metrics":{"Accuracy":"65.32","BLEU Score":"53.02"},"uses_additional_data":false,"paper_date":"2024-02-29","paper":"/paper/leveraging-pre-trained-language-models-for-3","paper_url":"https://doi.org/10.1007/s40747-024-01373-8","paper_title":"Leveraging pre-trained language models for code generation","code":"https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"lpn (Ling et al., 2016)","metrics":{"Accuracy":"62.3","BLEU Score":"77.6"},"uses_additional_data":false,"paper_date":"2016-03-22","paper":"/paper/latent-predictor-networks-for-code-generation","paper_url":"http://arxiv.org/abs/1603.06744v2","paper_title":"Latent Predictor Networks for Code Generation","code":"https://github.com/deepmind/card2code","n_code_links":2,"syntology":null},{"rank_in_archive_order":11,"model":"Phrasal Statistical MT (Ling et al., 2016)","metrics":{"Accuracy":"31.5","BLEU Score":"47.6"},"uses_additional_data":false,"paper_date":"2016-03-22","paper":"/paper/latent-predictor-networks-for-code-generation","paper_url":"http://arxiv.org/abs/1603.06744v2","paper_title":"Latent Predictor Networks for Code Generation","code":"https://github.com/deepmind/card2code","n_code_links":2,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":2,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":2,"n_unverified":5,"n_samples":7,"n_pointer_only_licence":2,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}