{"id":2001747,"updated":"2025-05-01T04:13:45.808293+00:00","links":{},"created":"2025-04-09T00:40:55.001015+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02001747","sets":["581:11839:11843"]},"path":["11843"],"owner":"80578","recid":"2001747","title":["機械学習に基づくバグ誘発リファクタリングの予測"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-04-15"},"_buckets":{"deposit":"e0e76911-2d59-4d8b-8122-9088f1a79ff2"},"_deposit":{"id":"2001747","pid":{"type":"depid","value":"2001747","revision_id":0},"owner":"80578","owners":[80578],"status":"published","created_by":80578},"item_title":"機械学習に基づくバグ誘発リファクタリングの予測","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習に基づくバグ誘発リファクタリングの予測","subitem_title_language":"ja"},{"subitem_title":"Prediction of Bug-inducing Refactorings Based on Machine Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:ソフトウェア工学（特選論文）] リファクタリング，バグ，機械学習，分類器，ソフトウェアメトリクス","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2025-04-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学情報理工学部"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"control_number":"2001747","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/2001747/files/IPSJ-JNL6604003.pdf","label":"IPSJ-JNL6604003.pdf"},"date":[{"dateType":"Available","dateValue":"2027-04-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6604003.pdf","filesize":[{"value":"1013.4 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"ddaf7b9a-a7ef-4235-adae-a8c0347452ff","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"丸山,勝久"}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Katsuhisa Maruyama","creatorNameLang":"en"}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"リファクタリングとは，ソフトウェアシステムの外部的振舞いを維持しつつ，その内部構造を変化させることで，それらの可読性や保守性を向上させる作業である．残念ながら，自動化されたリファクタリングの数には限りがあるため，バグを混入させる恐れのある手動リファクタリングは避けられない．このような状況において，適用したリファクタリングが将来のバグ修正を誘発した可能性を開発者が迅速に把握することができれば，リファクタリングを適用した直後にバグを取り除く可能性が高くなる．本論文では，18個の機械学習アルゴリズムに基づく予測モデルの構築を通して，バグを誘発するリファクタリングの予測性能を評価した実験結果を示す．結果として，リファクタリングインスタンスごとに収集した変更コードのメトリクス値を含む学習データを利用することで，ROC-AUCの値が0.962程度の予測モデルが得られることが分かった．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Refactoring is the process of improving the readability and maintainability of software systems by changing their internal structure while preserving their external behavior. Unfortunately, manual refactoring that may introduce bugs is unavoidable due to the limited number of automated refactorings. In such situations, if developers can quickly figure out the possibility of the applied refactorings to induce future bug fixes, they are likely to remove bugs immediately after the application of the refactorings. This paper presents the experimental results of evaluating the performance of prediction for refactorings that induce bugs through the construction of prediction models based on 18 machine-learning algorithms. The results showed that a prediction model with a ROC-AUC value of around 0.962 could be obtained using the training data that contain metrics values of the modified code collected for each refactoring instance.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"646","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"632","bibliographicIssueDates":{"bibliographicIssueDate":"2025-04-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"66"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/0002001747","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"80578"}}