{"updated":"2025-01-20T02:31:44.108046+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186617","sets":["1164:1384:9436:9437"]},"path":["9437"],"owner":"11","recid":"186617","title":["高脅威メモリリークのバイナリレベル動的検出法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-02"},"_buckets":{"deposit":"20a0a2f3-575b-43ab-a1f4-e4f812dfe83f"},"_deposit":{"id":"186617","pid":{"type":"depid","value":"186617","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"高脅威メモリリークのバイナリレベル動的検出法","author_link":["419198","419197","419196"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"高脅威メモリリークのバイナリレベル動的検出法"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"実行解析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-03-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学"},{"subitem_text_value":"東京工業大学"},{"subitem_text_value":"東京工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","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/186617/files/IPSJ-SE18198027.pdf","label":"IPSJ-SE18198027.pdf"},"date":[{"dateType":"Available","dateValue":"2020-03-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SE18198027.pdf","filesize":[{"value":"710.3 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":"5ebaa9a5-68e2-440b-8e27-3d7a52f5be3b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小泉, 雄太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒堀, 喜貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"権藤, 克彦"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112981","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, 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":"メモリリークは,不要なオブジェクトが将来増えることはない低脅威リークと,不要なオブジェクトが将来増え続ける高脅威リークに脅威度の観点から分類できる.この分類は一定の基準でまとめられたオブジェクト群 (グループ) にも適用可能である.従来の Staleness 解析のような全てのオブジェクトに対して特定の指標でリークの評価 / 報告をするリーク検出手法では,高脅威リークが低脅威リークの報告に埋没する可能性や,高脅威リークの即時判定ができないなどの問題がある.また,バイナリレベルでの解析においては,型情報のようなグループ化に適した情報の取得が困難な現状がある.本研究の提案手法である Pikelet は,バイナリコードを対象に高脅威のリークを高精度に検出することを目的とした動的メモリリーク検出手法である.高脅威リークの漸次的な特性から,グループサイズの成長過程を測定することで高脅威リークのグループを高精度で検出する.また,バイナリ解析で実現可能なグループ化の手段としてオブジェクト割り付け時の calling context を用いる.オブジェクトのグループ化と,グループの成長から脅威度を導出することで,Pikelet は新たに生成されたオブジェクトの危険度を即時判断し,プログラムに差し迫った脅威をより正確に報告する.実用プログラムを対象とする実験の結果,Pikelet は既存研究に比べて高脅威リークオブジェクト群の検知において精度の向上を示した (同一実行内での計測結果の平均で Recall は 22 ポイント,Precision は 80 ポイントの精度向上を達成した).また,実行オーバーヘッドは既存手法と同程度に収まることを確認した.","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":"2018-03-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"27","bibliographicVolumeNumber":"2018-SE-198"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:53:33.485561+00:00","id":186617,"links":{}}