Relevant bibliographies by topics / Source code plagiarism detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Source code plagiarism detection'

Author: Grafiati
Published: 11 December 2022
Last updated: 25 January 2023

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Journal articles on the topic "Source code plagiarism detection"

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Shen, Victor R. L. "Novel Code Plagiarism Detection Based on Abstract Syntax Tree and Fuzzy Petri Nets." International Journal of Engineering Education 1, no. 1 (2019): 46–56. http://dx.doi.org/10.14710/ijee.1.1.46-56.

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Those students who major in computer science and/or engineering are required to design program codes in a variety of programming languages. However, many students submit their source codes they get from the Internet or friends with no or few modifications. Detecting the code plagiarisms done by students is very time-consuming and leads to the problems of unfair learning performance evaluation. This paper proposes a novel method to detect the source code plagiarisms by using a high-level fuzzy Petri net (HLFPN) based on abstract syntax tree (AST). First, the AST of each source code is generated after the lexical and syntactic analyses have been done. Second, token sequence is generated based on the AST. Using the AST can effectively detect the code plagiarism by changing the identifier or program statement order. Finally, the generated token sequences are compared with one another using an HLFPN to determine the code plagiarism. Furthermore, the experimental results have indicated that we can make better determination to detect the code plagiarism.
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Awale, Nishesh, Mitesh Pandey, Anish Dulal, and Bibek Timsina. "Plagiarism Detection in Programming Assignments using Machine Learning." September 2020 2, no. 3 (2020): 177–84. http://dx.doi.org/10.36548/jaicn.2020.3.005.

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Plagiarism in programming assignments has been increasing these days which affects the evaluation of students. Thispaper proposes a machine learning approach for plagiarism detection of programming assignments. Different features related to source code are computed based on similarity score of n-grams, code style similarity and dead codes. Then, xgboost model is used for training and predicting whether a pair of source code are plagiarised or not. Many plagiarism techniques ignores dead codes such as unused variables and functions in their predictions tasks. But number of unused variables and functions in the source code are considered in this paper. Using our features, the model achieved an accuracy score of 94% and average f1-score of 0.905 on the test set. We also compared the result of xgboost model with support vector machines(SVM) and report that xgboost model performed better on our dataset.
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Budiman, Ariel, and Oscar Karnalim. "Automated Hints Generation for Investigating Source Code Plagiarism and Identifying The Culprits on In-Class Individual Programming Assessment." Computers 8, no. 1 (2019): 11. http://dx.doi.org/10.3390/computers8010011.

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Most source code plagiarism detection tools only rely on source code similarity to indicate plagiarism. This can be an issue since not all source code pairs with high similarity are plagiarism. Moreover, the culprits (i.e., the ones who plagiarise) cannot be differentiated from the victims even though they need to be educated further on different ways. This paper proposes a mechanism to generate hints for investigating source code plagiarism and identifying the culprits on in-class individual programming assessment. The hints are collected from the culprits’ copying behaviour during the assessment. According to our evaluation, the hints from source code creation process and seating position are 76.88% and at least 80.87% accurate for indicating plagiarism. Further, the hints from source code creation process can be helpful for indicating the culprits as the culprits’ codes have at least one of our predefined conditions for the copying behaviour.
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Shu, Bo, and Xiao Jun Du. "Software Source Code Plagiarism and Direction Detection Based on PDG." Applied Mechanics and Materials 373-375 (August 2013): 1172–77. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1172.

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Because of the complexity of the software development, some software developers may plagiarize source code that comes from other projects or open source software in order to shorten development cycle. Usually the copyist would modify and disguise the source code copied to escape plagiarism detection. So far, most algorithms cant completely detect the source disguised by the copyist, especially cant exactly distinguish between the source code and the plagiaristic code. In this paper, we summarize and analyze the effect of disguised source to the detection process, design the strategy to remove the effect of disguised source, and propose a PDG-based software source code plagiarism detection algorithm. The algorithm can detect the existence of disguised source, so as to find out source code plagiarism. And we propose a heuristic rule to make the detection algorithm have the ability to give the plagiarism direction. Any existing algorithm does not have this function. We prove the availability of the algorithm by experiment.
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Franclinton, Ricardo, and Oscar Karnalim. "A Language-Independent Library for Observing Source Code Plagiarism." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (2019): 110. http://dx.doi.org/10.20473/jisebi.5.2.110-119.

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Background: Most source code plagiarism detection tools are not modifiable. Consequently, when a modification is required to be applied, a new detection tool should be created along with it. This could be a problem as creating the tool from scratch is time-inefficient while most of the features are similar across source code plagiarism detection tools.Objective: To alleviate researchers' effort, this paper proposes a library for observing two plagiarism-suspected codes (a feature which is similar across most source code plagiarism detection tools).Methods: Unique to this library, it is not constrained by the selected programming language for development. It is executed from command line, which is supported by most programming languages.Results: According to our evaluation, the library is integrable and functional. Moreover, the library can enhance teaching assistants' accuracy and reduce the tasks' completion time.Conclusion: The library can be beneficial for the development of source code plagiarism detection tools since it is integrable, functional, and helpful for teaching assistants.Keywords:Language independency, Plagiarism detection, Reusable library, Source code, Tool development
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Rahal, Imad, and Colin Wielga. "Source Code Plagiarism Detection Using Biological String Similarity Algorithms." Journal of Information & Knowledge Management 13, no. 03 (2014): 1450028. http://dx.doi.org/10.1142/s0219649214500282.

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Source code plagiarism is easy to commit but difficult to catch. Many approaches have been proposed in the literature to automate its detection; however there is little consensus on what works best. In this paper, we propose two new measures for determining the accuracy of a given technique and describe an approach to convert code files into strings which can then be compared for similarity in order to detect plagiarism. We then compare several string comparison techniques, heavily utilised in the area of biological sequence alignment, and compare their performance on a large collection of student source code containing various types of plagiarism. Experimental results show that the compared techniques succeed in matching a plagiarised file to its original files upwards of 90% of the time. Finally, we propose a modification for these algorithms that drastically improves their runtimes with little or no effect on accuracy. Even though the ideas presented herein are applicable to most programming languages, we focus on a case study pertaining to an introductory-level Visual Basic programming course offered at our institution.
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Laxman, Mrs Ghuge Madhuri. "Java Source Code Plagiarism Detection System." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (2018): 3596–600. http://dx.doi.org/10.22214/ijraset.2018.3748.

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Lawal Abba, Hadiza, Abubakar Roko, Aminu B. Muhammad, Abdulgafar Usman, and Abba Almu. "Enhanced Semantic Similarity Detection of Program Code Using Siamese Neural Network." International Journal of Advanced Networking and Applications 14, no. 02 (2022): 5353–60. http://dx.doi.org/10.35444/ijana.2022.14205.

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Even though there are various source code plagiarism detection approaches, most of them are only concerned with lexical similarities attack with an assumption that plagiarism is only conducted by students who are not proficient in programming. However, plagiarism is often conducted not only due to student incapability but also because of bad time management. Thus, semantic similarity attacks should be detected and evaluated. This research proposes a source code semantic similarity detection approach that can detect most source code similarities by representing the source code into an Abstract Syntax Tree (AST) and evaluating similarity using a Siamese neural network. Since AST is a language-dependent feature, the SOCO dataset is selected which consists of C++ program codes. Based on the evaluation, it can be concluded that our approach is more effective than most of the existing systems for detecting source code plagiarism. The proposed strategy was implemented and an experimental study based on the AI-SOCO dataset revealed that the proposed similarity measure achieved better performance for the recommendation system in terms of precision, recall, and f1 score by 15%, 10%, and 22% respectively in the 100,000 datasets. In the future, it is suggested that the system can be improved by detecting inter-language source code similarity.
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Všianský, Richard, Dita Dlabolová, and Tomáš Foltýnek. "Source code plagiarism detection for PHP language." European Journal of Business Science and Technology 3, no. 2 (2017): 106–17. http://dx.doi.org/10.11118/ejobsat.v3i2.100.

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Karnalim, Oscar, and Aldi Aldiansyah. "Python Source Code Plagiarism Attacks in Object-Oriented Environment." Computer Engineering and Applications Journal 6, no. 3 (2017): 87–84. http://dx.doi.org/10.18495/comengapp.v6i3.217.

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Since source code plagiarism is an emerging issue on Computer Science major and Python is a new popular programming language, this paper aims to empirically enlist plagiarism attacks that might be occurred on Python source code. As our case study, our work will be focused on source code plagiarism in object-oriented environment. The result of this work is expected to become either an evaluation baseline or a prior knowledge for developing Python-targeted plagiarism detection system. Based on 280 plagiarism-suspected pairs that were extracted from four Basic Data Structure classes, four findings can be deducted. First, there are 20 distinct Python plagiarism attacks that might be occurred in object-oriented environment. Second, plagiarism attack trend on both object-oriented and procedural environment are considerably similar to each other. Third, there is no need to handle plagiarism attacks in both object-oriented and procedural environment separately. Last, plagiarism attacks in object-oriented environment is more monotonous than such attacks in procedural environment.
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Dissertations / Theses on the topic "Source code plagiarism detection"

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Bergman, Adam. "Automatic Detection of Source Code Plagiarism in Programming Courses." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104883.

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Source code plagiarism is an ongoing problem in programming courses at higher academic institutions. For this reason, different automated source code plagiarism detection tools have been developed. However, they require several manual steps before the submissions can be compared. Linnaeus University uses GitLab to handle their students’ code-related assignments but lacks an integrated workflow for checking submissions against plagiarism. Instead, Linnaeus University’s plagiarism-checking process is done manually, which is a challenging and time-consuming process. This thesis is a case study on Linnaeus University, focusing on integrating one of the plagiarism detection tools with GitLab using Continuous integration pipelines. The objectives have been to collect students’ submissions, communicate with the plagiarism tool, and visually present the results within GitLab. The prototype has been evaluated by a set of manually created submissions with different levels of plagiarism to ensure that the detection tool differentiates plagiarized and non-plagiarized submissions. Teachers at Linnaeus University have tested the workflow and reasoned whether the prototype fulfills their requirements.
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Holma, Niklas. "Program Dependence Graph Generation and Analysis for Source Code Plagiarism Detection." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-87446.

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Systems and tools that finds similarities among essays and reports are widely used by todays universities and schools to detect plagiarism. Such tools are however insufficient when used for source code comparisons since they are fragile to the most simplest forms of diguises. Other methods that analyses intermediate forms such as token strings, syntax trees and graph representations have shown to be more effective than using simple textual matching methods. In this master thesis report we discuss how program dependence graphs, an abstract representation of a programs semantics, can be used to find similar procedures. We also present an implementation of a system that constructs approximated program dependence graphs from the abstract syntax tree representation of a program. Matching procedures are found by testing graph pairs for either sub-graph isomorphism or graph monomorphism depending on whether structured transfer of control has been used. Under a scenario based evaluation our system is compared to Moss, a popular plagiarism detection tool. The result shows that our system is more or least as effective than Moss in finding plagiarized procedured independently on the type of modifications used.<br>System och verktyg som hittar likheter mellan uppsatser och rapporter används i stor omfattning av dagens universitet och skolor för att hitta plagiat bland studenters inlämningar. Sådana verktyg är dock otillräckliga när de används för att jämföra programkod eftersom de är svaga mot de enklaste formerna av modifikationer. Andra metoder som analyserar mellanstegsformer såsom tokensträngar, syntaxträd och grafrepresentationer har visat sig vara mer effektiva än att använda sig av enkla textuella metoder. I denna examensuppsats diskuterar vi hur programberoendegrafer, en abstrakt representation av en programs semantik, kan användas för att hitta jämförelsevis liknande procedurer. Vi presenterar också ett system som konstruerar approximerade programberoendegrafer från det abstrakta syntaxträdet av ett program. Matchande procedurer hittas genom att testa grafpar för antingen sub-graf isomorfism eller monomorfism beroende på om strukturerad byte av kontrolflöde har använts. I en scenariobaserad utvärdering jämför vi vårt system mot Moss, ett populärt verktyg för att detektera plagiat. Resultaten visar att vårt system är lika eller mer effektivt som Moss att detektera plagierade procedurer oberoende av de typer av modifikationer som använts.
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Tennyson, Matthew Francis. "Authorship Attribution of Source Code." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/322.

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Authorship attribution of source code is the task of deciding who wrote a program, given its source code. Applications include software forensics, plagiarism detection, and determining software ownership. A number of methods for the authorship attribution of source code have been presented in the past. A review of those existing methods is presented, while focusing on the two state-of-the-art methods: SCAP and Burrows. The primary goal was to develop a new method for authorship attribution of source code that is even more effective than the current state-of-the-art methods. Toward that end, a comparative study of the methods was performed in order to determine their relative effectiveness and establish a baseline. A suitable set of test data was also established in a manner intended to support the vision of a universal data set suitable for standard use in authorship attribution experiments. A data set was chosen consisting of 7,231 open-source and textbook programs written in C++ and Java by thirty unique authors. The baseline study showed both the Burrows and SCAP methods were indeed state-of-the-art. The Burrows method correctly attributed 89% of all documents, while the SCAP method correctly attributed 95%. The Burrows method inherently anonymizes the data by stripping all comments and string literals, while the SCAP method does not. So the methods were also compared using anonymized data. The SCAP method correctly attributed 91% of the anonymized documents, compared to 89% by Burrows. The Burrows method was improved in two ways: the set of features used to represent programs was updated and the similarity metric was updated. As a result, the improved method successfully attributed nearly 94% of all documents, compared to 89% attributed in the baseline. The SCAP method was also improved in two ways: the technique used to anonymize documents was changed and the amount of information retained in the source code author profiles was determined differently. As a result, the improved method successfully attributed 97% of anonymized documents and 98% of non-anonymized documents, compared to 91% and 95% that were attributed in the baseline, respectively. The two improved methods were used to create an ensemble method based on the Bayes optimal classifier. The ensemble method successfully attributed nearly 99% of all documents in the data set.
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Cosma, Georgina. "An approach to source-code plagiarism detection investigation using latent semantic analysis." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/3575/.

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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.
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Mengoli, Chiara. "Plagiarism." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20928/.

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This document aims to provide an overview of the current situation of plagiarism. This survey presents a taxonomy of various plagiarism forms and includes discussion on each of these forms. Moreover, it presents the existence of various tools and systems for detecting plagiarism. The easy access to the Web, its increasing use, and the development of technologies have led to the need to adapt plagiarism laws, implement new mechanisms anti-plagiarism or mechanisms of detection and empower people on the problem of plagiarism. Therefore, there will be a dedicated section for each of these aspects.
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Hattingh, F., AK Buitendag, and der Walt JS van. "Presenting an alternative source code plagiarism detection framework for improving the teaching and learning of programming." Journal of Information Technology Education: Innovations in Practice, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001990.

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Abstract The transfer and teaching of programming and programming related skills has become, increasingly difficult on an undergraduate level over the past years. This is partially due to the number of programming languages available as well as access to readily available source code over the Web. Source code plagiarism is common practice amongst many undergraduate students. This practice has a detrimental effect on the presentation of specific content relating to introduction to programming courses. One of the problems identified in the research conducted is that turnaround time with relation to assessment and feedback, which are presented to the students, is a critical factor in the subsequent success rates of the subject. This paper investigates, utilizing a literature review, how plagiarism detection metrics and a framework for providing effective feedback to students and educators could be implemented to enhance the teaching and learning processes. The predominant technique used for detecting plagiarism is to evaluate how a piece of source code was constructed over time. By analyzing the students’ programming patterns, lectures can be adapted to address problem areas and react accordingly. The paper also provides an overview of current metrics used for plagiarism detection and suggests ways of improving the process by including enhanced techniques for the gathering of metrics over time as well as suggesting ways to use the metrics to aid learning on all cognitive levels. Some of the key considerations presented as part of this research include effective feedback mechanisms and real-time responses to plagiarism as well as contributing towards learning on different cognitive levels.
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Nečadová, Anežka. "Detekce plagiátů programových kódů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221352.

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This semestral thesis presents definition of plagiarism and focuses primarily on solving this problem in academic world. The main topic is the detection of plagiarism. It is discussed the various steps of the detection process and special attention is given to plagiarism detection of program codes. The work mentions question of the reliability of detection tools and divides the plagiarism detection methods into basic groups. One chapter is devoted metrics for comparing files. Mentioned are two tools available to detect plagiarism. In the last chapter is analyzed own draft program for plagiarism detection of program codes. The detector was applied to a database of student’s works, and the results were plotted.
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Maciel, Danilo Leal. "Sherlock N-Overlap: normalization invasive and overlap coefficient for analysis of similarity between source code in programming disciplines." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12195.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior<br>This work is contextualized in the problem of plagiarism detection among source codes in programming classes. Despite the wide set of tools available for the detection of plagiarism, only few tools are able to effectively identify all lexical and semantic similarities between pairs of codes, because of the complexity inherent to this type of analysis. Therefore to the problem and the scenario in question, it was made a study about the main approaches discussed in the literature on detecting plagiarism in source code and as a main contribution, conceived to be a relevant tool in the field of laboratory practices. The tool is based on Sherlock algorithm, which has been enhanced as of two perspectives: firstly, with changes in the similarity coefficient used by the algorithm in order to improve its sensitivity for comparison of signatures; secondly, proposing intrusive techniques preprocessing that, besides eliminating irrelevant information, are also able to overemphasize structural aspects of the programming language, or gathering separating strings whose meaning is more significant for the comparison or even eliminating sequences less relevant to highlight other enabling better inference about the degree of similarity. The tool, called Sherlock N-Overlap was subjected to rigorous evaluation methodology, both in simulated scenarios as classes in programming, with results exceeding tools currently highlighted in the literature on plagiarism detection.<br>Este trabalho se contextualiza no problema da detecÃÃo de plÃgio entre cÃdigos-fonte em turmas de programaÃÃo. Apesar da ampla quantidade de ferramentas disponÃveis para a detecÃÃo de plÃgio, poucas sÃo capazes de identificar, de maneira eficaz, todas as semelhanÃas lÃxicas e semÃnticas entre pares de cÃdigos, o que se deve à complexidade inerente a esse tipo de anÃlise. Fez-se, portanto, para o problema e o cenÃrio em questÃo, um estudo das principais abordagens discutidas na literatura sobre detecÃÃo de plÃgio em cÃdigo-fonte e, como principal contribuiÃÃo, concebeu-se uma ferramenta aplicÃvel no domÃnio de prÃticas laboratoriais. A ferramenta tem por base o algoritmo Sherlock, que foi aprimorado sob duas perspectivas: a primeira, com modificaÃÃes no coeficiente de similaridade usado pelo algoritmo, de maneira a melhorar a sua sensibilidade para comparaÃÃo de assinaturas; a segunda, propondo tÃcnicas de prÃ-processamento invasivas que, alÃm de eliminar informaÃÃo irrelevante, sejam tambÃm capazes de sobrevalorizar aspectos estruturais da linguagem de programaÃÃo, reunindo ou separando sequÃncias de caracteres cujo significado seja mais expressivo para a comparaÃÃo ou, ainda, eliminando sequÃncias menos relevantes para destacar outras que permitam melhor inferÃncia sobre o grau de similaridade. A ferramenta, denominada Sherlock N-Overlap, foi submetida a rigorosa metodologia de avaliaÃÃo, tanto em cenÃrios simulados como em turmas de programaÃÃo, apresentando resultados superiores a ferramentas atualmente em destaque na literatura sobre detecÃÃo de plÃgio.
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Liaqat, Ahmad Gull, and Aijaz Ahmad. "Plagiarism Detection in Java Code." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13231.

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Chilowicz, Michel. "Recherche de similarité dans du code source." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00587628.

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La duplication de code source a de nombreuses origines : copie et adaptation inter-projets ou clonage au sein d'un même projet. Rechercher des correspondances de code copié permet de le factoriser dans un projet ou de mettre en évidence des situations de plagiat. Nous étudions des méthodes statiques de recherche de similarité sur du code ayant potentiellement subi des opérations d'édition telle que l'insertion, la suppression, la transposition ainsi que la factorisation et le développement de fonctions. Des techniques d'identification de similarité génomique sont examinées et adaptées au contexte de la recherche de clones de code source sous forme lexemisée. Après une discussion sur des procédés d'alignement de lexèmes et de recherche par empreintes de n-grams, est présentée une méthode de factorisation fusionnant les graphes d'appels de fonctions de projets au sein d'un graphe unique avec introduction de fonctions synthétiques exprimant les correspondances imbriquées. Elle utilise des structures d'indexation de suffixes pour la détermination de facteurs répétés. Une autre voie d'exploration permettant de manipuler de grandes bases indexées de code par arbre de syntaxe est abordée avec la recherche de sous-arbres similaires par leur hachage et leur indexation selon des profils d'abstraction variables. Des clones exacts de sous-arbres de forte proximité dans leurs arbres d'extraction peuvent alors être consolidés afin d'obtenir des correspondances approchées et étendues. En amont et en aval de la recherche de correspondances, des métriques de similarité sont définies afin de préselectionner les zones d'examen, affiner la recherche ou mieux représenter les résultats
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Books on the topic "Source code plagiarism detection"

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Lawson, Bruce D. Diurnal variation in the fine fuel moisture code: Tables and computer source code. Canada-British Columbia Partnership Agreement on Forest Resource Development, 1996.

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Book chapters on the topic "Source code plagiarism detection"

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Ďuračík, Michal, Emil Kršák, and Patrik Hrkút. "Source Code Representations for Plagiarism Detection." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95522-3_6.

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Jain, Amay Dilip, Ankur Gupta, Diksha Choudhary, Nayan, and Ashish Tiwari. "A Comprehensive Source Code Plagiarism Detection Software." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0475-2_30.

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Pandit, Anala A., and Gaurav Toksha. "Review of Plagiarism Detection Technique in Source Code." In International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_38.

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Yasmeen, Syed, Munjuluri Prathyusha, Malisetty Rajeswari, Padmanabhuni Srujana, and K. Ashesh. "Plagiarism Detection for Source Codes and Texts." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5157-1_17.

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Manahi, Mohammed, Suriani Sulaiman, and Normi Sham Awang Abu Bakar. "Source Code Plagiarism Detection Using Siamese BLSTM Network and Embedding Models." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8515-6_31.

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Tominaga, Hiroyuki, and Kazushi Ueta. "Plagiarism Detection Methods by Similarity and Its Application to Source Codes of C Program." In Proceedings in Information and Communications Technology. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54436-4_22.

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Jiang, Hao, and Zhemin Jiang. "The Study of Plagiarism Detection for Program Code." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22456-0_19.

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Srivastava, Swati, Akshit Rai, and Mahima Varshney. "A Tool to Detect Plagiarism in Java Source Code." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7345-3_20.

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John, Samuel, and George Boateng. "“I didn’t copy his code”: Code Plagiarism Detection with Visual Proof." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78270-2_37.

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Wierda, Andreas, Eric Dortmans, and Lou Somers. "Pattern Detection in Object-Oriented Source Code." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88655-6_11.

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Conference papers on the topic "Source code plagiarism detection"

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"Review of Source Code Plagiarism Detection Techniques." In 4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022). Cihan University, 2022. http://dx.doi.org/10.24086/cocos2022/paper.658.

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In the educational sector, where scientific publications and articles are concerned, plagiarism detection systems are critical. Plagiarism occurs when someone copies a piece of content without the author's permission or citation. You'll need a lot of knowledge about plagiarism types and classes to detect such conduct. Thanks to recently developed tools and procedures, many types of plagiarism may now be recognized. Plagiarism detection has become a crucial concern as a result of advancements in information and communication technology tools (ICT Tools), as well as the availability of online scientific materials. Plagiarism detection has become a crucial issue with the availability of several software text editors. Plagiarism detection and the various types of plagiarism detection datasets used in identification systems has already been the subject of numerous research investigations. This paper discusses various types of plagiarism and various source code plagiarism detection tools developed. Despite the fact that there has been extensive study into systems for detecting source code plagiarism for many years, there is still a need to investigate a robust method.
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"Performance Evaluation of Source Code Plagiarism Detection System." In 4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022). Cihan University, 2022. http://dx.doi.org/10.24086/cocos2022/paper.732.

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Plagiarism Detection Systems are particularly useful in identifying plagiarism in the educational sector, where scientific publications and articles are common. Plagiarism occurs when someone replicates a piece of work without permission or citation from the original creator. Because of the advancement of communication and information technologies (ICT) and the accessibility of scientific materials on the internet, plagiarism detection has become a top priority and due to the broad availability of freeware text editors, detecting source code plagiarism has become a big difficulty. There have already been several research on the many forms of plagiarism detection algorithms used in identification systems, as well as source code plagiarism detection. This work suggests a strategy that combines TF-IDF transformations with a Random Forest Classifier to achieve a 93.5 percent accuracy rate, which is high when compared to previous strategies. The suggested system is implemented using the Python programming language.
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Kustanto, Cynthia, and Inggriani Liem. "Automatic Source Code Plagiarism Detection." In 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2009. http://dx.doi.org/10.1109/snpd.2009.62.

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Poon, Jonathan Y. H., Kazunari Sugiyama, Yee Fan Tan, and Min-Yen Kan. "Instructor-centric source code plagiarism detection and plagiarism corpus." In the 17th ACM annual conference. ACM Press, 2012. http://dx.doi.org/10.1145/2325296.2325328.

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Duracik, Michal, Emil Krsak, and Patrik Hrkut. "Scalable Source Code Plagiarism Detection Using Source Code Vectors Clustering." In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2018. http://dx.doi.org/10.1109/icsess.2018.8663708.

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Petrik, Juraj, Daniela Chuda, and Branislav Steinmuller. "Source code plagiarism detection: The Unix way." In 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2017. http://dx.doi.org/10.1109/sami.2017.7880355.

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Misic, Marko J., Jelica Z. Protic, and Milo V. Tomasevic. "Improving source code plagiarism detection: Lessons learned." In 2017 25th Telecommunication Forum (TELFOR). IEEE, 2017. http://dx.doi.org/10.1109/telfor.2017.8249481.

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Li, Xiao, and Xiao Jing Zhong. "The Source Code Plagiarism Detection Using AST." In 2010 International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). IEEE, 2010. http://dx.doi.org/10.1109/iptc.2010.90.

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Brixtel, Romain, Mathieu Fontaine, Boris Lesner, Cyril Bazin, and Romain Robbes. "Language-Independent Clone Detection Applied to Plagiarism Detection." In 2010 10th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2010. http://dx.doi.org/10.1109/scam.2010.19.

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Zakova, K., J. Pistej, and P. Bistak. "Online tool for student's source code plagiarism detection." In 2013 IEEE 11th International Conference on Emerging eLearning Technologies and Applications (ICETA). IEEE, 2013. http://dx.doi.org/10.1109/iceta.2013.6674469.

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