{"id":20,"date":"2021-02-06T21:32:47","date_gmt":"2021-02-06T21:32:47","guid":{"rendered":"http:\/\/www.cs.ubbcluj.ro\/quadeep\/?page_id=20"},"modified":"2021-02-06T21:32:47","modified_gmt":"2021-02-06T21:32:47","slug":"project-plan","status":"publish","type":"page","link":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/project-plan\/","title":{"rendered":"Project Plan"},"content":{"rendered":"\n<p>The work plan is divided into five work packages with dependencies and expected interactions among them, following the usual stages needed to reach the project\u2019s objectives.<\/p>\n\n\n\n<h3 class=\"has-background\" style=\"background-color:#f0f0f0\"><strong>WP1<\/strong> Determine a taxonomy of defect types and prepare case studies for software defect prediction<\/h3>\n\n\n\n<ul><li><strong>T1.1<\/strong> &#8211; Conduct a literature review to identify current defect type taxonomies and approaches for their analysis<\/li><li><strong>T1.2<\/strong> &#8211; Determine&nbsp; a taxonomy of defect types using unsupervised learning applied on open-source data sets\/software systems<\/li><li><strong>T1.3<\/strong> &#8211; Perform software quality&nbsp; analysis on the systems identified at T1.2<\/li><li><strong>T1.4<\/strong> &#8211; Prepare appropriate case studies that will be used for defect prediction, according&nbsp; to the established bug taxonomy<\/li><\/ul>\n\n\n\n<h3 class=\"has-background\" style=\"background-color:#f0f0f0\"><strong>WP2<\/strong> Establish machine learning based methods for feature engineering in software defect prediction<\/h3>\n\n\n\n<ul><li><strong>T2.1<\/strong> &#8211; Literature review on existing methods for automatic feature learning, coupling and cohesion-based features for software defect prediction<\/li><li><strong>T2.2<\/strong> &#8211; Developing deep learning methods for learning semantic&nbsp; features from software artifacts<\/li><li><strong>T2.3<\/strong> &#8211; Experimental&nbsp; evaluation and analysis of the learned features<\/li><li><strong>T2.4<\/strong> &#8211; Defining coupling and cohesion-based software metrics for software defect prediction<\/li><li><strong>T2.5<\/strong> &#8211; Experimental&nbsp; evaluation and analysis of the coupling and cohesion-based software metrics &nbsp;&nbsp;<\/li><\/ul>\n\n\n\n<h3 class=\"has-background\" style=\"background-color:#f0f0f0\"><strong>WP3<\/strong> Develop new deep learning algorithms software defect prediction<\/h3>\n\n\n\n<ul><li><strong>T3.1<\/strong> &#8211; Conduct literature review to identify current machine learning results in software defect prediction<\/li><li><strong>T3.2<\/strong> &#8211; Develop one-class classification approaches for defect prediction<\/li><li><strong>T3.3<\/strong> &#8211; Develop one-shot learning approaches for defect prediction<\/li><li><strong>T3.4<\/strong> &#8211; Experimentally evaluate deep learning techniques and compare them with existing approaches<\/li><\/ul>\n\n\n\n<h3 class=\"has-background\" style=\"background-color:#f0f0f0\"><strong>WP4<\/strong> Development of QuaDeeP<\/h3>\n\n\n\n<ul><li><strong>T4.1<\/strong> &#8211; Design the QuaDeeP system using an incremental development process<\/li><li><strong>T4.2<\/strong> &#8211; Development of defect prediction functionality<\/li><li><strong>T4.3<\/strong> &#8211; Testing and final validation of the QuaDeeP prototype<\/li><\/ul>\n\n\n\n<h3 class=\"has-background\" style=\"background-color:#f0f0f0\"><strong>WP5<\/strong> Project management<\/h3>\n\n\n\n<div class=\"wp-container-69f54ddd7a82c wp-block-group\"><div class=\"wp-block-group__inner-container\">\n<ul><li><strong>T5.1<\/strong> &#8211; Project coordination<\/li><li><strong>T5.2<\/strong> &#8211; Dissemination<\/li><\/ul>\n\n\n\n<p><\/p>\n<\/div><\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The work plan is divided into five work packages with dependencies and expected interactions among them, following the usual stages&hellip; <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/20"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/comments?post=20"}],"version-history":[{"count":16,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/20\/revisions"}],"predecessor-version":[{"id":44,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/20\/revisions\/44"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/media?parent=20"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}