Syntax tree fingerprinting: a foundation for source code similarity detection

Plagiarism detection and clone refactoring in software depend on one common concern: nding similar source chunks across large repositories. However, since code duplication in software is often the result of copy-paste behaviors, only minor modi cations are expected between shared codes. On the contrary, in a plagiarism detection context, edits are more extensive and exact matching strategies show their limits. Among the three main representations used by source code similarity detection tools, namely the linear token sequences, the Abstract Syntax Tree (AST) and the Program Depen- dency Graph (PDG), we believe that the AST could e ciently support the program analysis and transformations required for the advanced similarity detection process. In this paper we present a simple and scalable architecture based on syntax tree nger- printing. Thanks to a study of several hashing strategies reducing false-positive collisions, we propose a framework that e ciently indexes AST representations in a database, that quickly detects exact (w.r.t source code abstraction) clone clusters and that easily retrieves their corresponding ASTs. Our aim is to allow further processing of neighboring exact matches in order to identify the larger approximate matches, dealing with the common modi cation patterns seen in the intra-project copy-pastes and in the plagiarism cases

On the detection of SOurce COde re-use

© {Owner/Author | ACM} {2014}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824878"This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE track, named SOurce COde Re-use (SOCO) focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total five teams submitted 17 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works on the posed shared task.PAN@FIRE (SOCO) has been organised in the framework of WIQ-EI (EC IRSES grantn. 269180) and DIANA-APPLICATIONS (TIN2012-38603-C02- 01) research projects. The work of the last author was supported by CONACyT Mexico Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349.Flores Sáez, E.; Rosso, P.; Moreno Boronat, LA.; Villatoro-Tello, E. (2014). On the detection of SOurce COde re-use. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 21-30. https://doi.org/10.1145/2824864.2824878S2130C. Arwin and S. Tahaghoghi. Plagiarism detection across programming languages. Proceedings of the 29th Australian Computer Science Conference, Australian Computer Society, 48:277--286, 2006.N. 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Barrón-Cedeño, P. Rosso, and L. Moreno. Towards the Detection of Cross-Language Source Code Reuse. Proceedings of 16th International Conference on Applications of Natural Language to Information Systems, NLDB-2011, Springer-Verlag, LNCS(6716), pages 250--253, 2011.E. Flores, M. Ibarra-Romero, L. Moreno, G. Sidorov, and P. Rosso. Modelos de recuperación de información basados en n-gramas aplicados a la reutilización de código fuente. In Proc. 3rd Spanish Conf. on Information Retrieval, pages 185--188, 2014.D. Ganguly and G. J. Jones. Dcu@ fire-2014: an information retrieval approach for source code plagiarism detection. In Fire [8].R. García-Hernández and Y. Lendeneva. Identification of similar source codes based on longest common substrings. In Fire [8].M. Joy and M. Luck. Plagiarism in programming assignments. Education, IEEE Transactions on, 42(2):129--133, May 1999.A. Marcus, A. Sergeyev, V. Rajlich, and J. Maletic. 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The System Kato: Detecting Cases of Plagiarism for Answer-Set Programs

Plagiarism detection is a growing need among educational institutions and solutions for different purposes exist. An important field in this direction is detecting cases of source-code plagiarism. In this paper, we present the tool Kato for supporting the detection of this kind of plagiarism in the area of answer-set programming (ASP). Currently, the tool is implemented for DLV programs but it is designed to handle other logic-programming dialects as well. We review the basic features of Kato, introduce its theoretical underpinnings, and discuss an application of Kato for plagiarism detection in the context of courses on logic programming at the Vienna University of Technology

An Abstract Method Linearization for Detecting Source Code Plagiarism in Object-Oriented Environment

Despite the fact that plagiarizing source code is a trivial task for most CS students, detecting such unethical behavior requires a considerable amount of effort. Thus, several plagiarism detection systems were developed to handle such issue. This paper extends Karnalim's work, a low-level approach for detecting Java source code plagiarism, by incorporating abstract method linearization. Such extension is incorporated to enhance the accuracy of low-level approach in term of detecting plagiarism in object-oriented environment. According to our evaluation, which was conducted based on 23 design-pattern source code pairs, our extended low-level approach is more effective than state-of-the-art and Karnalim's approach. On the one hand, when compared to state-of-the-art approach, our approach can generate less coincidental similarities and provide more accurate result. On the other hand, when compared to Karnalim's approach, our approach, at some extent, can generate higher similarity when simple abstract method invocation is incorporated.Comment: The 8th International Conference on Software Engineering and Service Scienc

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium 201

A comparative analysis of web-based GIS applications using usability metrics

With the rapid expansion of the internet, Web-based Geographic Information System (WGIS) applications have gained popularity, despite the interface of the WGIS application being difficult to learn and understand because special functions are needed to manipulate the maps. Hence, it is essential to evaluate the usability of WGIS applications. Usability is an important factor in ensuring the development of quality, usable software products. On the other hand, there are a number of standards and models in the literature, each of which describes usability in terms of various set of attributes. These models are vague and difficult to understand. Therefore, the primary purpose of this study is to compare five common usability models (Shackel, Nielsen, ISO 9241 P-11, ISO 9126-1 and QUIM) to identify usability metrics that have most frequently used in the previous models. The questionnaire method and the automated usability evaluation method by using Loop11 tool were used, in order to evaluate the usability metrics for three case studies of commonly used WGIS applications as Google maps, Yahoo maps, and MapQuest. Finally, those case studies were compared and analysed based on usability metrics that have been identified. Based on a comparative study, four usability metrics (Effectiveness, Efficiency, Satisfaction and Learnability) were identified. Those usability metrics were characterized by consistent, comprehensive, not vaguely and proper to evaluate the usability of WGIS applications. In addition, there was a positive correlation between these usability metrics. The comparative analysis indicates that Effectiveness, Satisfaction and Learnability were higher, and the Efficiency was lesser by using the Loop11 tool compared to questionnaire method for the three case studies. In addition, Yahoo Maps and MapQuest have usability metrics rate lesser than Google Maps by applying two methods. Therefore, Google Maps is more usable compared to Yahoo Maps and MapQuest