1,318 research outputs found
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
Issues Related to the Detection of Source Code Plagiarism in Students Assignments
Detecting similarity or plagiarism in the academic research publications, source code, etc. has been a long time complex and time consuming task. Several algorithms, tools and websites exist that try to find plagiarism or possible plagiarism in those human creative products. In this paper we used source code plagiarism detection tools to assess the level of plagiarism in source codes. We also investigated issues related to accuracy and challenges in detecting possible plagiarism in students\u27 assignments. In a second study, we evaluated some tools against detecting possible plagiarism in research papers. Results showed that such process or decision is not binary to make and that subjectivity is high. In addition, there is a need to tune plagiarism detection tools to give criticality or weights by users of those tools to categorize and classify different levels of seriousness for committing plagiarism
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 approach to source-code plagiarism detection investigation using latent semantic analysis
This thesis looks at three aspects of source-code plagiarism. The first aspect of the
thesis is concerned with creating a definition of source-code plagiarism; the second aspect
is concerned with describing the findings gathered from investigating the Latent Semantic
Analysis information retrieval algorithm for source-code similarity detection; and the final
aspect of the thesis is concerned with the proposal and evaluation of a new algorithm that
combines Latent Semantic Analysis with plagiarism detection tools.
A recent review of the literature revealed that there is no commonly agreed definition of
what constitutes source-code plagiarism in the context of student assignments. This thesis
first analyses the findings from a survey carried out to gather an insight into the perspectives
of UK Higher Education academics who teach programming on computing courses. Based
on the survey findings, a detailed definition of source-code plagiarism is proposed.
Secondly, the thesis investigates the application of an information retrieval technique,
Latent Semantic Analysis, to derive semantic information from source-code files. Various
parameters drive the effectiveness of Latent Semantic Analysis. The performance of Latent
Semantic Analysis using various parameter settings and its effectiveness in retrieving
similar source-code files when optimising those parameters are evaluated.
Finally, an algorithm for combining Latent Semantic Analysis with plagiarism detection
tools is proposed and a tool is created and evaluated. The proposed tool, PlaGate, is
a hybrid model that allows for the integration of Latent Semantic Analysis with plagiarism
detection tools in order to enhance plagiarism detection. In addition, PlaGate has a facility
for investigating the importance of source-code fragments with regards to their contribution
towards proving plagiarism. PlaGate provides graphical output that indicates the clusters of
suspicious files and source-code fragments
- …