{"created":"2025-01-19T01:17:47.652927+00:00","updated":"2025-01-19T15:33:03.333137+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00217294","sets":["1164:1384:10896:10897"]},"path":["10897"],"owner":"44499","recid":"217294","title":["深層学習を用いたコードクローン検出器のベンチマーク間精度調査"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-04"},"_buckets":{"deposit":"9965ecc8-b968-45db-b2f4-a8b6fb248b9a"},"_deposit":{"id":"217294","pid":{"type":"depid","value":"217294","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層学習を用いたコードクローン検出器のベンチマーク間精度調査","author_link":["562778","562773","562772","562780","562771","562779","562776","562775","562777","562774"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習を用いたコードクローン検出器のベンチマーク間精度調査"},{"subitem_title":"Investigating the Performance of Deep Learning-based Code Clone Detectors Using Multiple Benchmarks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソフトウェア工学における機械学習の利用(ML4SE)","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-03-04","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学"},{"subitem_text_value":"大阪大学"},{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"京都工芸繊維大学"},{"subitem_text_value":"大阪大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Kyoto Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/217294/files/IPSJ-SE22210008.pdf","label":"IPSJ-SE22210008.pdf"},"date":[{"dateType":"Available","dateValue":"2024-03-04"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SE22210008.pdf","filesize":[{"value":"1.3 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etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8825","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ソフトウェア保守において問題となる要因の 1 つとしてコードクローンがある．近年，多様なコードクローンを検出するために教師あり深層学習を取り入れた検出器が提案されている．既存の深層学習を用いたコードクローン検出器では，検出器の学習と評価のために 1 つのデータセットを分割して使用している．しかし，開発者が実開発現場でコードクローン検出器を用いる場合，検出器を適用する対象と学習データが類似しているとは限らない．そこで，本研究では学習と評価に複数のベンチマークを用いて，深層学習を用いたコードクローン検出器である CCLearner，ASTNN，CodeBERT に対して精度の調査を行った．その結果，全ての検出器で学習と異なるベンチマークに対し精度が下がり，CCLearner とASTNN ではクローンのタイプごとで精度が異なったことを確認した．また，ASTNN と CodeBERT では，学習データに類似するベンチマークで評価を行った時，精度が高かったことを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ソフトウェア工学（SE）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2022-SE-210"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":217294,"links":{}}