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

The Effectiveness of Low-Level Structure-based Approach Toward Source Code Plagiarism Level Taxonomy

Low-level approach is a novel way to detect source code plagiarism. Such approach is proven to be effective when compared to baseline approach (i.e., an approach which relies on source code token subsequence matching) in controlled environment. We evaluate the effectiveness of state of the art in low-level approach based on Faidhi \& Robinson's plagiarism level taxonomy; real plagiarism cases are employed as dataset in this work. Our evaluation shows that state of the art in low-level approach is effective to handle most plagiarism attacks. Further, it also outperforms its predecessor and baseline approach in most plagiarism levels.Comment: The 6th International Conference on Information and Communication Technolog

Structural analysis of source code plagiarism using graphs

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science. May 2017Plagiarism is a serious problem in academia. It is prevalent in the computing discipline where students are expected to submit source code assignments as part of their assessment; hence, there is every likelihood of copying. Ideally, students can collaborate with each other to perform a programming task, but it is expected that each student submit his/her own solution for the programming task. More so, one might conclude that the interaction would make them learn programming. Unfortunately, that may not always be the case. In undergraduate courses, especially in the computer sciences, if a given class is large, it would be unfeasible for an instructor to manually check each and every assignment for probable plagiarism. Even if the class size were smaller, it is still impractical to inspect every assignment for likely plagiarism because some potentially plagiarised content could still be missed by humans. Therefore, automatically checking the source code programs for likely plagiarism is essential. There have been many proposed methods that attempt to detect source code plagiarism in undergraduate source code assignments but, an ideal system should be able to differentiate actual cases of plagiarism from coincidental similarities that usually occur in source code plagiarism. Some of the existing source code plagiarism detection systems are either not scalable, or performed better when programs are modified with a number of insertions and deletions to obfuscate plagiarism. To address this issue, a graph-based model which considers structural similarities of programs is introduced to address cases of plagiarism in programming assignments. This research study proposes an approach to measuring cases of similarities in programming assignments using an existing plagiarism detection system to find similarities in programs, and a graph-based model to annotate the programs. We describe experiments with data sets of undergraduate Java programs to inspect the programs for plagiarism and evaluate the graph-model with good precision. An evaluation of the graph-based model reveals a high rate of plagiarism in the programs and resilience to many obfuscation techniques, while false detection (coincident similarity) rarely occurred. If this detection method is adopted into use, it will aid an instructor to carry out the detection process conscientiously.MT 201

Detecting Source Code Plagiarism on .NET Programming Languages using Low-level Representation and Adaptive Local Alignment

Even though there are various source code plagiarism detection approaches, only a few works which are focused on low-level representation for deducting similarity. Most of them are only focused on lexical token sequence extracted from source code. In our point of view, low-level representation is more beneficial than lexical token since its form is more compact than the source code itself. It only considers semantic-preserving instructions and ignores many source code delimiter tokens. This paper proposes a source code plagiarism detection which rely on low-level representation. For a case study, we focus our work on .NET programming languages with Common Intermediate Language as its low-level representation. In addition, we also incorporate Adaptive Local Alignment for detecting similarity. According to Lim et al, this algorithm outperforms code similarity state-of-the-art algorithm (i.e. Greedy String Tiling) in term of effectiveness. According to our evaluation which involves various plagiarism attacks, our approach is more effective and efficient when compared with standard lexical-token approach

Plagiarism detection in source programs using structural similarities

The paper presents a plagiarism detection framework the goal of which is to determine whether two programs are similar to each other, and if so, to what extent. The issue of plagiarism detection has been considered earlier for written material, such as student essays. For these, text-based algorithms have been published. We argue that in case of program code comparison, structure based techniques may be much more suitable. The main idea is to transform the source code into mathematical objects, use appropriate reduction and comparison methods on these, and interpret the results appropriately. We have designed a generic program structure comparison framework and implemented it for the Prolog and SML programming languages. We have been using the implementation at BUTE to successfully detect plagiarism in homework assignments for years

Machine Learning Approaches on External Plagiarism Detection

External plagiarism detection is a technique that refers to the comparison between suspicious document and different sources. External plagiarism models are generally preceded by candidate document retrieval and further analysis and then performed to determine the plagiarism occurring. Currently most of the external plagiarism detection is using similarity measurement approaches that are expressed by a pair of sentences or phrase considered similar. Similarity techniques approach is more easily understood using a formula which compares term or token between the two documents. In contrast to the approach of machine learning techniques which refer to the pattern matching and cannot directly comparing token or term between two documents. This paper proposes some machine learning techniques such as k-nearest neighbors (KNN), support vector machine (SVM) and artificial neural network (ANN) for external plagiarism detection and comparing the result with Cosine similarity measurement approach. This paper presented density based that normalized by frequency as the pattern. The result showed that all machine learning approach used in this experiment has better performance in term of accuracy, precision and recall

The Plagiarism-Proof Policy Handbook: A Multidimensional-Systems Approach To Foster Student Writing Accountability and Prevent Plagiarism In College and University Classrooms

Academic integrity, ethical research, and proper documentation practices are key values emphasized within most post-secondary academic communities. To support positive student outcomes, colleges and universities use honor codes, policy measures, and plagiarism detection systems to deter plagiarism, believing that the above measures promote ethical behavior from the students who graduate from their institutions. This thesis first recommends a broader, environmentally focused approach which examines the micro, mezzo, and macro levels of the post-secondary educational setting within universities and colleges using systems theory and strengths theory. It then identifies some important plagiarism barriers and plagiarism enablers students encounter within each environmental context above. By connecting these approaches with vital information on authorial identity, care ethics, writing-process accountability, and policy and curriculum recommendations, this author recommends a multidimensional framework to ameliorate several environmental factors that feed the plagiarism problem in post-secondary educational settings. An important undercurrent running throughout this project is that of making authentic writing more rewarding than plagiarism in post-secondary classrooms. This thesis project culminates in small handbook of policy designed to assist various stakeholders in their efforts to reduce student plagiarism. Furthermore, it includes suggestions for curriculum and policy adjustments such as teaching Arthur Costa & Kallick’s sixteen habits of mind (2008) within the university curriculum and requiring instructor-led writing practice lab classes on campus. It also includes ways various stakeholders can cooperate to effectively solve the plagiarism problem