{"created":"2025-01-19T00:50:45.318064+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00183210","sets":["6164:6165:6522:9235"]},"path":["9235"],"owner":"11","recid":"183210","title":["機械学習による不具合組み合わせ特定への自動分類法の提案と評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-08-23"},"_buckets":{"deposit":"9cddeba9-7fd6-49c9-a44f-4ac9c6958ab2"},"_deposit":{"id":"183210","pid":{"type":"depid","value":"183210","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"機械学習による不具合組み合わせ特定への自動分類法の提案と評価","author_link":["401386","401387","401388"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習による不具合組み合わせ特定への自動分類法の提案と評価"}]},"item_type_id":"18","publish_date":"2017-08-23","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":"京都工芸繊維大学 大学院工芸科学研究科 情報工学専攻/産業技術総合研究所 情報技術研究部門"},{"subitem_text_value":"産業技術総合研究所 情報技術研究部門"},{"subitem_text_value":"京都工芸繊維大学 情報工学・人間科学系"}]},"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/183210/files/IPSJ-SES2017008.pdf","label":"IPSJ-SES2017008.pdf"},"date":[{"dateType":"Available","dateValue":"2019-08-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SES2017008.pdf","filesize":[{"value":"449.5 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":"c722a0e6-8967-47a8-911a-c31486cb2c11","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西浦, 生成"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"崔, 銀惠"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"水野, 修"}],"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":"不具合組み合わせ特定とは,組み合わせテストの各テストケースの実行結果の成否から,バグを含むと思われるパラメータ値の組み合わせを特定する問題である.我々は,機械学習を用いた不具合組み合わせ特定に取り組んでいる.本研究では,まず,組み合わせテストケースに含まれるパラメータ値の組み合わせとテスト結果の成否をモデルとしたロジスティック回帰分析を行い,それによって得られた回帰係数値から,各パラメータ値の組み合わせが不具合組み合わせである疑わしさを決定する.次に,各パラメータ値の組み合わせの疑わしさから,それが不具合組み合わせであるか否かを自動分類するために,境界値決定法,最大距離分割法,K-means 法の 3 つのクラスタリング手法を適用する.最後に,実際にバグを含むオープンソースプロジェクトのプログラム flex,grep,make のテストスイートに対して提案法を適用した比較評価実験を行うことで,提案法の有効性を示す.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"34","bibliographic_titles":[{"bibliographic_title":"ソフトウェアエンジニアリングシンポジウム2017論文集"}],"bibliographicPageStart":"25","bibliographicIssueDates":{"bibliographicIssueDate":"2017-08-23","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":183210,"updated":"2025-01-20T03:45:01.707909+00:00","links":{}}