{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-neural-networks-over-control","title":"Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction","arxiv_id":"1802.04986","date":"2018-02-14","proceeding":null,"authors":["Anh Viet Phan","Minh Le Nguyen","Lam Thu Bui"],"abstract":"Existing defects in software components is unavoidable and leads to not only\na waste of time and money but also many serious consequences. To build\npredictive models, previous studies focus on manually extracting features or\nusing tree representations of programs, and exploiting different machine\nlearning algorithms. However, the performance of the models is not high since\nthe existing features and tree structures often fail to capture the semantics\nof programs. To explore deeply programs' semantics, this paper proposes to\nleverage precise graphs representing program execution flows, and deep neural\nnetworks for automatically learning defect features. Firstly, control flow\ngraphs are constructed from the assembly instructions obtained by compiling\nsource code; we thereafter apply multi-view multi-layer directed graph-based\nconvolutional neural networks (DGCNNs) to learn semantic features. The\nexperiments on four real-world datasets show that our method significantly\noutperforms the baselines including several other deep learning approaches.","url_abs":"http://arxiv.org/abs/1802.04986v1","url_pdf":"http://arxiv.org/pdf/1802.04986v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convolutional-neural-networks-over-control","repo_url":"https://github.com/nguyenlab/DGCNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}