{"updated":"2025-01-20T05:02:12.490134+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00178657","sets":["581:8997:9002"]},"path":["9002"],"owner":"11","recid":"178657","title":["改版履歴の分析に基づく変更支援手法における時間的近接性の考慮と同一作業コミットの統合による影響"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-04-15"},"_buckets":{"deposit":"c399344a-8f57-4092-87fc-6af2dbdbb6b3"},"_deposit":{"id":"178657","pid":{"type":"depid","value":"178657","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"改版履歴の分析に基づく変更支援手法における時間的近接性の考慮と同一作業コミットの統合による影響","author_link":["383233","383232","383230","383231","383235","383234"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"改版履歴の分析に基づく変更支援手法における時間的近接性の考慮と同一作業コミットの統合による影響"},{"subitem_title":"An Empirical Study of the Effects of Recency-aware History Analysis and Commits Aggregation on Change Guide","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）] 変更支援，ソフトウェアリポジトリマイニング，改版履歴，ソフトウェア保守","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2017-04-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学大学院情報理工学研究科計算工学専攻"},{"subitem_text_value":"東京工業大学大学院情報理工学研究科計算工学専攻／The School of Computer Science and Communication KTH Royal Institute of Technology"},{"subitem_text_value":"東京工業大学情報理工学院情報工学系"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Science Graduate School of Information Science and Engineering, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science Graduate School of Information Science and Engineering, Tokyo Institute of Technology / The School of Computer Science and Communication KTH Royal Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science School of Computing, Tokyo Institute of 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/178657/files/IPSJ-JNL5804005.pdf","label":"IPSJ-JNL5804005.pdf"},"date":[{"dateType":"Available","dateValue":"2019-04-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5804005.pdf","filesize":[{"value":"2.1 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":"80f2b2d5-e1f5-48e3-96c7-778810914756","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森, 達也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"アンダース, ハグワード"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 隆志"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tatsuya, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Anders, Hagward","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Kobayashi","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":"改版履歴の分析によってファイルの共変更ルールを抽出し，必要な変更箇所を推薦することで開発者への支援を行う研究が進められている．既存手法による推薦は正確であるが，多くの場合に変更漏れを推薦できないという問題点がある．本研究ではより多くの変更漏れを開発者に推薦するため，改版履歴を分析して共変更ルールを抽出するうえで考慮すべき2つの特性に着目した．1つは時間的近接性である．ソフトウェアの進化にともないソフトウェアの依存関係は変化するため，そこから得られる共変更ルールも変化しうる．全コミットではなく最近のコミットのみを分析の対象とすることで，共変更ルールの質の向上が期待できる．もう1つは同一作業コミットの統合である．同一作業に関するコミットを統合することで，コミットの粒度が統一され，共変更ルールの質向上につながると考える．我々は，この2つの特性が共変更ルールの質にどのような影響を及ぼすかを調査した．代表的なOSSを用いた評価実験により，共変更ルールが時間とともに変化すること，および，時間的近接性の考慮によってより多くの変更漏れを推薦できることを明らかにした．特にEclipseにおいては，Recallが最大で0.11から0.28まで上昇した．また，同一作業コミットを統合することが，推薦性能の向上に有益であることを明らかにした．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Many studies on change guide, which suggest necessary code changes with co-change rules extracted from a change history, have been performed so far. Recommendations by existing approaches are adequately accurate, however, those approaches often fail to detect candidates of overlooked changes. In this study, we focus on two characteristics to recommend more overlooked changes. One is recency. Some of software dependencies used for change guide become obsolete along with long-term evolution. We use only recent commits for extracting co-change rules to avoid incorrect suggestions stemming from such obsolete dependencies. The other is commits aggregation. The granularity of commits depends on the nature of developers and projects. We aggregate commits for the same task to capture actual co-change relations, which expects to improve the quality of co-change rules. We investigate the effects of the two characteristics on the quality of co-change rules. Empirical results using typical OSS show that co-change rules vary over time and we can detect more overlooked changes by focusing on recency. Particularly, in the Eclipse case, the maximum Recall improved up to 0.28 by recency-aware history analysis whereas the one of the baseline is 0.11. The results also show that commits aggregation for the same task can improve the recommendation performance.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"817","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"807","bibliographicIssueDates":{"bibliographicIssueDate":"2017-04-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"58"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:47:59.446989+00:00","id":178657,"links":{}}