Model clone detection for rule-based model transformation languages

Strüber, Daniel; Acretoaie, Vlad; Plöger, Jennifer

doi:10.1007/s10270-017-0625-6

Cited by 9 publications

(10 citation statements)

References 60 publications

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“…However, they are generally designed with a different purpose: finding similar intra-model (and to a lesser extent inter-model) fragments generated by a legitimate copy and paste reuse. Approaches for model clone detection have been proposed on UML models (Störrle, 2015), graphs (Pham, Nguyen, Nguyen, Al-Kofahi, & Nguyen, 2009), Simulink models (Deissenboeck, Hummel, Juergens, Pfaehler, & Schaetz, 2010) or rule-based model transformations (Strüber, Acret ¸oaie, & Plöger, 2017) while other proposals target general model comparison (Brun & Pierantonio, 2008), model alignment (Falleri, Huchard, Lafourcade, & Nebut, 2008;Kolovos, 2009) or model versioning (Constant, 2012). In theory, all these works could be systematically executed on all combinations of models in a repository to find suspicious pairs of models but in practice this is not really feasible.…”

Section: Related Workmentioning

confidence: 99%

Efficient plagiarism detection for software modeling assignments

Martínez

Wimmer

Cabot

2020

Computer Science Education

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Background and Context: Reports suggest plagiarism is a common occurrence in universities. While plagiarism detection mechanisms exist for textual artifacts, this is less so for non-code related ones such as software design artifacts like models, metamodels or model transformations. Objective: To provide an efficient mechanism for the detection of plagiarism in repositories of Model-Driven Engineering (MDE) assignments. Method: Our approach is based on the adaptation of the Locality Sensitive Hashing, an approximate nearest neighbor search mechanism, to the modeling technical space. We evaluate our approach on a real use case consisting of two repositories containing 10 years of student answers to MDE course assignments. Findings: We have found that: (i) effectively, plagiarism occurred on the aforementioned course assignments (ii) our tool was able to efficiently detect them. Implications: Plagiarism detection must be integrated into the toolset and activities of MDE instructors in order to correctly evaluate students.

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Section: Related Workmentioning

confidence: 99%

Efficient plagiarism detection for software modeling assignments

Martínez

Wimmer

Cabot

2020

Computer Science Education

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“…Syntactic techniques are either metric-based or tree-based. Tree-based (e.g., [2]) and graph-based techniques (e.g., [22]) parse the code into abstract syntax trees and find cloned code parts using tree-matching algorithms. Metric-based ones (e.g.…”

Section: Related Workmentioning

confidence: 99%

Detecting Security Vulnerabilities using Clone Detection and Community Knowledge

Viertel¹,

Brunotte²,

Strüber³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Faced with the severe financial and reputation implications associated with data breaches, enterprises now recognize security as a top concern for software analysis tools. While software engineers are typically not equipped with the required expertise to identify vulnerabilities in code, community knowledge in the form of publicly available vulnerability databases could come to their rescue. For example, the Common Vulnerabilities and Exposures Database (CVE) contains data about already reported weaknesses. However, the support with available examples in these databases is scarce. CVE entries usually do not contain example code for a vulnerability, its exploit or patch. They just link to reports or repositories that provide this information. Manually searching these sources for relevant information is time-consuming and error-prone. In this paper, we propose a vulnerability detection approach based on community knowledge and clone detection. The key idea is to harness available example source code of software weaknesses, from a large-scale vulnerability database, which are matched to code fragments using clone detection. We leverage a clone detection technique from the literature, which we adapted to make it applicable to vulnerability databases. In an evaluation based on 20 reports and affected projects, our approach showed good precision and recall.

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“…UML models are also a growing target for MCD [15], [17]. More recently, researchers are working on approaches for MCD in EMF metamodels [18], and model transformation languages [19]. The information provided by model clone analysis includes pattern clustering [11], [20], anti pattern detection [21], security and quality analysis [22], [23], and more.…”

Section: Background -Model Clone Detectionmentioning

confidence: 99%

MoCoP: Towards a Model Clone Portal

Babur

Stephan

2019

2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE)

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Model clone detection for rule-based model transformation languages

Cited by 9 publications

References 60 publications

Efficient plagiarism detection for software modeling assignments

Efficient plagiarism detection for software modeling assignments

Detecting Security Vulnerabilities using Clone Detection and Community Knowledge

MoCoP: Towards a Model Clone Portal

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