Timur Saglam, Sebastian Hahner, Jan Willem Wittler, Thomas Kühn
{"title":"元模型的基于token的抄袭检测","authors":"Timur Saglam, Sebastian Hahner, Jan Willem Wittler, Thomas Kühn","doi":"10.1145/3550356.3556508","DOIUrl":null,"url":null,"abstract":"Plagiarism is a widespread problem in computer science education. Manual inspection is impractical for large courses, and the risk of detection is thus low. Many plagiarism detectors are available for programming assignments. However, very few approaches are available for modeling assignments. To remedy this, we introduce token-based plagiarism detection for metamodels. To this end, we extend the widely-used software plagiarism detector JPlag. We evaluate our approach with real-world modeling assignments and generated plagiarisms based on obfuscation attack classes. The results show that our approach outperforms the state-of-the-art.","PeriodicalId":182662,"journal":{"name":"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Token-based plagiarism detection for metamodels\",\"authors\":\"Timur Saglam, Sebastian Hahner, Jan Willem Wittler, Thomas Kühn\",\"doi\":\"10.1145/3550356.3556508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plagiarism is a widespread problem in computer science education. Manual inspection is impractical for large courses, and the risk of detection is thus low. Many plagiarism detectors are available for programming assignments. However, very few approaches are available for modeling assignments. To remedy this, we introduce token-based plagiarism detection for metamodels. To this end, we extend the widely-used software plagiarism detector JPlag. We evaluate our approach with real-world modeling assignments and generated plagiarisms based on obfuscation attack classes. The results show that our approach outperforms the state-of-the-art.\",\"PeriodicalId\":182662,\"journal\":{\"name\":\"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3550356.3556508\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3550356.3556508","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Plagiarism is a widespread problem in computer science education. Manual inspection is impractical for large courses, and the risk of detection is thus low. Many plagiarism detectors are available for programming assignments. However, very few approaches are available for modeling assignments. To remedy this, we introduce token-based plagiarism detection for metamodels. To this end, we extend the widely-used software plagiarism detector JPlag. We evaluate our approach with real-world modeling assignments and generated plagiarisms based on obfuscation attack classes. The results show that our approach outperforms the state-of-the-art.