MAF: Method-Anchored Test Fragmentation for Test Code Plagiarism DetectionSEET
Software engineering education becomes popular due to the rapid development of the software industry. In order to reduce learning costs and improve learning efficiency, some online practice platforms have emerged. This paper proposes a novel test code plagiarism detection technology, namely MAF, by introducing bidirectional static slicing to anchor methods under test and extract fragments of test codes. Combined with similarity measures, MAF can achieve effective plagiarism detection by avoiding a large amount of unrelated noisy test codes. The experiment is conducted on the dataset of Mooctest, which so far has supported hundreds of test activities around the world in the past 3 years. The experimental results show that MAF can effectively improve the performance (precision, recall and F1-measure) of similarity measures for test code plagiarism detection. We believe that MAF can further expand and promote software testing education, and it can also be extended to use in test recommendation, test reuse and other engineering applications.
Thu 30 May Times are displayed in time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | Assessment in the ClassroomSoftware Engineering Education and Training / Posters at St-Denis / Notre-Dame Chair(s): Ivana BosnićUniversity of Zagreb, Faculty of Electrical Engineering and Computing | ||
14:00 15mTalk | MAF: Method-Anchored Test Fragmentation for Test Code Plagiarism DetectionSEET Software Engineering Education and Training Weisong SunState Key Laboratory for Novel Software Technology, Nanjing University, Xingya WangState Key Laboratory for Novel Software Technology, Nanjing University, Haoran WuState Key Laboratory for Novel Software Technology, Nanjing University, Ding DuanState Key Laboratory for Novel Software Technology, Nanjing University, Zesong SunState Key Laboratory for Novel Software Technology, Nanjing University, Zhenyu ChenNanjing University | ||
14:15 6mPoster | A Grading Schema for Reinforcing Teamwork Quality in a Capstone Course Posters Cecilia Bastarrica, Daniel PerovichDepartment of Computer Science, University of Chile, Francisco J. Gutierrez, Maíra MarquesDepartment of Computer Science, University of Chile | ||
14:21 10mTalk | Simulating Student Mistakes to Evaluate the Fairness of Automated GradingSEET Software Engineering Education and Training Benjamin CleggThe University of Sheffield, Siobhán NorthThe University of Sheffield, Phil McMinnUniversity of Sheffield, Gordon FraserUniversity of Passau | ||
14:31 15mTalk | Automatic Grading of Programming Assignments: A Formal Semantics Based ApproachSEET Software Engineering Education and Training Xiao LiuThe Pennsylvania University, University Park, Shuai WangETH Zurich, Pei WangPennsylvania State University, Dinghao WuPennsylvania State University | ||
14:46 10mTalk | Experience Report on a Move to Techniques-oriented Student Project GradingSEET Software Engineering Education and Training Siim KarusUniversity of Tartu | ||
14:56 34mTalk | Author Panel DiscussionSEET Software Engineering Education and Training |