\u5c0f\u6797\u78142021\u5e74\u4fee\u58eb\u4fee\u4e86\u306eChen\u3055\u3093\u304c\uff0c\u4fee\u58eb\u8ad6\u6587\u306e\u5185\u5bb9\u3092\u767a\u5c55\u3055\u305b\u305f\u5185\u5bb9\u3092 The 21st IEEE International Conference on Quality Software (QRS 2021)\u306b\u3066\u7814\u7a76\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f.<\/p>\n
\u8457\u8005\uff1aChen Lang, Jidong Li, Takashi Kobayashi
\n\u984c\u76ee\uff1a Software Defect Prediction via Multi-Channel Convolutional Neural Network
\n\u63b2\u8f09\u8a8c\uff1a The 21st IEEE International Conference on Quality Software (QRS 2021), Dec. 6-10, 2021
\n\u6982\u8981\uff1a
\nWith the growing complexity of modern software, the expense of improving software reliability becomes significant. To reduce costs, Software Defect Prediction (SDP) was proposed to detect hidden bugs a few decades ago. Recent studies demon-strate the superiority of the deep learning-based approach in SDP, such as Convolutional Neural Network (CNN). However, the noise in defect data can still deteriorate their performance. To enhance the performance of the state-of-the-art CNN-based method, we propose an improved SDP framework named Defect Prediction via Multi-Channel Convolutional Neural Network (DP-MC-CNN). The novelty is that DP-MC-CNN extracts sub-trees from the Abstract Syntax Tree, generates multiple paths, and feeds them to a multi-channel CNN, which weakens the noise effect. In evaluation, we assess DP-MC-CNN on seven projects by average F1 score, and it outperforms the state-of-the-art CNN-based method by 3.93%. Furthermore, we validate the effectiveness of the multi-channel approach and reveal the influences of each sub-tree component.<\/p><\/blockquote>\n<\/p>","protected":false},"excerpt":{"rendered":"
\u5c0f\u6797\u78142021\u5e74\u4fee\u58eb\u4fee\u4e86\u306eChen\u3055\u3093\u304c\uff0c\u4fee\u58eb\u8ad6\u6587\u306e\u5185\u5bb9\u3092\u767a\u5c55\u3055\u305b\u305f\u5185\u5bb9\u3092 The 21st IEEE International Conference on Quality Software (QRS 2021)\u306b\u3066\u7814 […]<\/p>\n","protected":false},"author":19,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-2310","post","type-post","status-publish","format-standard","hentry","category-research"],"_links":{"self":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/posts\/2310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/comments?post=2310"}],"version-history":[{"count":3,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/posts\/2310\/revisions"}],"predecessor-version":[{"id":2324,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/posts\/2310\/revisions\/2324"}],"wp:attachment":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/media?parent=2310"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/categories?post=2310"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/ja\/wp-json\/wp\/v2\/tags?post=2310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}