{"updated":"2025-01-20T06:52:53.260723+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00174353","sets":["6164:6165:6522:8893"]},"path":["8893"],"owner":"11","recid":"174353","title":["回帰結合ニューラルネットワークを利用したAPI推薦手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-08-24"},"_buckets":{"deposit":"2ac71b4e-7cd1-44af-9a32-70c4455e731a"},"_deposit":{"id":"174353","pid":{"type":"depid","value":"174353","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"回帰結合ニューラルネットワークを利用したAPI推薦手法","author_link":["357454"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"回帰結合ニューラルネットワークを利用したAPI推薦手法"},{"subitem_title":"An API Suggestion using Recurrent Neural Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"プログラム理解","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2016-08-24","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nihon University","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/174353/files/IPSJ-SES2016008.pdf","label":"IPSJ-SES2016008.pdf"},"date":[{"dateType":"Available","dateValue":"2018-08-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SES2016008.pdf","filesize":[{"value":"491.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"12"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9ec817e8-4123-4aa5-96b2-a3d058f72544","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山本, 哲男"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ソースコードを記述していく際,開発者は,効率よくプログラムを作成するために既存のソースコードの再利用やライブラリを活用して開発を行う.そこで,本研究では,既存のソースコードに記述されているメソッド呼び出し文の順序に着目し,メソッド呼び出し文を補完する手法について提案する.本手法では,回帰結合ニューラルネットワーク (recurrent neural netowork) を利用し,次に現れるであろうメソッド呼び出し文を予測する.さらに,提案する手法を実装し,10 プロジェクトのオープンソースソフトウェアを用いて補完候補の精度を計測した.また,回帰結合ニューラルネットワークの様々なパラメータが実験結果にどのように影響するかを調査し,補完候補の精度がどのように変化するかについても実験した.実験の結果,典型的なサンプルソースコードの補完においては,38%の精度で補完候補の一位に必要なメソッド呼び出し文が現れることが確認できた.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"33","bibliographic_titles":[{"bibliographic_title":"ソフトウェアエンジニアリングシンポジウム2016論文集"}],"bibliographicPageStart":"25","bibliographicIssueDates":{"bibliographicIssueDate":"2016-08-24","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2016"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:44:33.800108+00:00","id":174353,"links":{}}