{"id":2310,"date":"2021-12-08T04:39:28","date_gmt":"2021-12-07T19:39:28","guid":{"rendered":"http:\/\/www.sa.c.titech.ac.jp\/?p=2310"},"modified":"2022-03-17T13:34:09","modified_gmt":"2022-03-17T04:34:09","slug":"%e5%9b%bd%e9%9a%9b%e4%bc%9a%e8%ad%b0-software-defect-prediction-via-multi-channel-convolutional-neural-network","status":"publish","type":"post","link":"https:\/\/www.sa.comp.isct.ac.jp\/en\/archives\/2310","title":{"rendered":"[Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network"},"content":{"rendered":"<p>Chen Lang presented our paper in The 21st IEEE International Conference on Quality Software (QRS 2021).<\/p>\n<blockquote><p>Authors: Chen Lang, Jidong Li, Takashi Kobayashi<\/p>\n<p>Title: Software Defect Prediction via Multi-Channel<\/p>\n<p>Book Title: Proc.\u00a0The 21st IEEE International Conference on Quality Software (QRS 2021), Dec. 6-10, 2021<\/p>\n<p>Abstract:<br \/>\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><\/p>","protected":false},"excerpt":{"rendered":"<p>Chen Lang presented our paper in The 21st IEEE International Conference on Quality Software (QRS 2021). Author [&hellip;]<\/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\/en\/wp-json\/wp\/v2\/posts\/2310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/comments?post=2310"}],"version-history":[{"count":3,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/posts\/2310\/revisions"}],"predecessor-version":[{"id":2324,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/posts\/2310\/revisions\/2324"}],"wp:attachment":[{"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/media?parent=2310"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/categories?post=2310"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sa.comp.isct.ac.jp\/en\/wp-json\/wp\/v2\/tags?post=2310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}