{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00083946","sets":["581:6644:6865"]},"path":["6865"],"owner":"11","recid":"83946","title":["目視評価と判別モデルを組み合わせたfault-proneモジュールのランク付け手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-09-15"},"_buckets":{"deposit":"b61be937-0dc4-4e56-b728-505f6766c251"},"_deposit":{"id":"83946","pid":{"type":"depid","value":"83946","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"目視評価と判別モデルを組み合わせたfault-proneモジュールのランク付け手法","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"目視評価と判別モデルを組み合わせたfault-proneモジュールのランク付け手法"},{"subitem_title":"An Approach for Prioritizing Fault-prone Modules by Combining Manual Inspection and Discriminant Model","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文] fault-proneモジュール，判別モデル，サポートベクタマシン，目視評価","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2012-09-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"三菱電機株式会社通信機製作所／奈良先端科学技術大学院大学"},{"subitem_text_value":"静岡大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Mitsubishi Electric Co. Communication Systems Center / Graduate School of Information Science, Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Shizuoka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science, Nara Institute of Science and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/83946/files/IPSJ-JNL5309025.pdf"},"date":[{"dateType":"Available","dateValue":"2014-09-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5309025.pdf","filesize":[{"value":"1.2 MB"}],"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":"a4e52a9f-b9f1-4bfc-b3ba-8f36645d3dc5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"笠井, 則充"},{"creatorName":"森崎, 修司"},{"creatorName":"松本, 健一"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Norimitsu, Kasai","creatorNameLang":"en"},{"creatorName":"Shuji, Morisaki","creatorNameLang":"en"},{"creatorName":"Ken-ichi, Matsumoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"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_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":"不具合を含む可能性の高いモジュールから順に受入れ検査を実施することを目的とし，fault-prone判別モデルの判別得点とソースコードの目視評価とを組み合わせたモジュールのランク付け手法を提案する．提案手法では，判別モデルから得た判別得点によりモジュールをランク付けし，上位αのモジュールを目視評価の結果により再度ランク付けする．判別モデルと目視評価の組み合わせによるランク付けの精度を評価することを目的として，商用ソフトウェアを対象として，判別モデルの判別得点順，モジュールの規模順，ランダム順と目視評価を組み合わせた場合のランク付けの精度をαの値を変化させて評価した．ランク付けの精度はAUC（Area Under the Curve: Alberg diagramの曲線下面積）により比較した．いずれの組み合わせにおいてもαの値を大きくすることにより相対的にAUCが大きくなるという結果が得られ，判別モデルとの組み合わせにおいて最も大きなAUCとなった．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper proposes an approach for prioritizing modules by their fault-proneness for acceptance test. The approach combines results of a fault-prone discriminant model and inspection by developers. First, the approach ranks modules by fault-proneness according to a discriminant model. Then, modules included in the top α are inspected with questions. To evaluate the effectiveness of the approach, we conducted a case study with commercial software. In the case study, manual inspection is combined with fault-proneness by a discriminant model, size of each module and random. The accuracies are compared by the values of area under the curve of Alberg diagram with five α values. The result shows all accuracies are increased by combining inspection in three trials. In the case study, the accuracy is largest by the combination of fault-prone discriminant model and manual inspection.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2290","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"2279","bibliographicIssueDates":{"bibliographicIssueDate":"2012-09-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"53"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":83946,"updated":"2025-01-21T18:06:48.626790+00:00","links":{},"created":"2025-01-18T23:37:17.517246+00:00"}