Identification of Simulink model antipattern instances using model clone detection

Stephan, Matthew; Cordy, James R.

doi:10.1109/models.2015.7338258

Cited by 13 publications

(3 citation statements)

References 25 publications

(65 reference statements)

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“…Two tools, Artshopr (Thomas et al 2016) and SLRefactor (Tran & Dziobek 2013), were implemented to address these bad smells. Stephan and Cordy adopted near-miss cross-clone detection to find instances of antipatterns derived from the literature in public Simulink projects (Stephan & Cordy 2015).…”

Section: Related Workmentioning

confidence: 99%

Evolution of Bad Smells in LabVIEW Graphical Models.

Popoola¹,

Zhao²,

Gray³

2021

JOT

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Bad smells often indicate potential problems in software, which may lead to long-term challenges and expensive maintenance efforts. Although bad smells often occur in source code, bad smells also exist in representations of design descriptions and models. We have observed that many users of graphical modeling environments (e.g., LabVIEW) are systems engineers who may not be aware of core software engineering techniques, such as refactoring of bad smells. Systems engineers often focus on implementation correctness and may be unaware of how their designs affect long-term maintenance properties that may increase design smells. There exists a large body of research focused on analysing bad smells embedded in the source code of textual languages, but there has been limited research on bad smells in systems models of graphical languages.In this paper, we present a semi-automated approach for extracting design smells across versions of LabVIEW graphical models through user-defined queries. We describe example queries that highlight the emergence of design smells that we discovered from posts in the LabVIEW user's forum. We then demonstrate the use of the example queries in understanding the evolution of seven bad smells we found in 81 LabVIEW models stored in 10 GitHub repositories. We analyze the evolution of these smells in order to understand the prevalence and introduction of bad smells, as well as the relationship between bad smells and the structural changes made to the models. Our results show that all of the models contain instances of at least one type of bad smell and the number of smells fluctuates as the size of a model increases. Furthermore, the majority of the structural changes across different versions of LabVIEW models involve the addition of new elements with a corresponding increase in the presence of design smells. This paper summarizes the need for better analysis of design smells in systems models and suggests an approach that may assist in improving the structure and quality of systems models developed in LabVIEW.

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Section: Related Workmentioning

confidence: 99%

Evolution of Bad Smells in LabVIEW Graphical Models.

Popoola¹,

Zhao²,

Gray³

2021

JOT

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“…Though there have been previous model collections, they lack fine-grained meta-information, are not self-contained, and are not redistributable due to restrictive or missing licenses-making them hard or impossible to use for most empirical researchers. Given the lack of such a collection, the few existing empirical studies of Simulink models have been limited to proprietary models or a small number of public models [9,41,42].…”

Section: Introductionmentioning

confidence: 99%

SLNET: A Redistributable Corpus of 3rd-party Simulink Models

Shrestha,

Chowdhury,

Csallner

2022

Preprint

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MATLAB/Simulink is widely used for model-based design. Engineers create Simulink models and compile them to embedded code, often to control safety-critical cyber-physical systems in automotive, aerospace, and healthcare applications. Despite Simulink's importance, there are few large-scale empirical Simulink studies, perhaps because there is no large readily available corpus of thirdparty open-source Simulink models. To enable empirical Simulink studies, this paper introduces SLNET, the largest corpus of freely available third-party Simulink models. SLNET has several advantages over earlier collections. Specifically, SLNET is 8 times larger than the largest previous corpus of Simulink models, includes finegrained metadata, is constructed automatically, is self-contained, and allows redistribution. SLNET is available under permissive open-source licenses and contains its collection and analysis tools. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; Model-driven software engineering; • Computer systems organization → Embedded and cyber-physical systems.

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“…One example of this is model clone detection, which provides engineers the ability to detect similar models and elements in modeling projects [13]. Model clone detection has a variety of uses including library extraction, model maintenance [14], and quality assurance [15,16,17]. Because there are many different modeling abstractions, research in model clone detection is evolving quickly and is yielding many different tools and techniques targeting different model types.As more model clone detectors are created, the need for model clone detector evaluation becomes imperative.…”

mentioning

confidence: 99%

MuMonDE: A framework for evaluating model clone detectors using model mutation analysis

Stephan

Cordy

2018

Software Testing Verif & Rel

Self Cite

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Summary Model‐driven engineering is an increasingly prevalent approach in software engineering where models are the primary artifacts throughout a project's life cycle. A growing form of analysis and quality assurance in these projects is model clone detection, which identifies similar model elements. As model clone detection research and tools emerge, methods must be established to assess model clone detectors and techniques. In this paper, we describe the MuMonDE framework, which researchers and practitioners can use to evaluate model clone detectors using mutation analysis on the models each detector is geared towards. MuMonDE applies mutation testing in a novel way by randomly mutating model elements within existing projects to emulate various types of clones that can exist within that domain. It consists of 2 main phases. The mutation phase involves determining the mutation targets, selecting the appropriate mutation operations, and injecting mutants. The second phase, evaluation, involves detecting model clones, preprocessing clone reports, analyzing those reports to calculate recall and precision, and visualizing the data. We introduce MuMonDE by describing each phase in detail. We present our experiences and examples in successfully developing a MuMonDE implementation capable of evaluating Simulink model clone detectors. We validate MuMonDE by demonstrating its ability to answer evaluation questions and provide insights based on the data it generates. With this research using mutation analysis, our goal is to improve model clone detection and its analytical capabilities, thus improving model‐driven engineering as a whole.

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Identification of Simulink model antipattern instances using model clone detection

Cited by 13 publications

References 25 publications

Evolution of Bad Smells in LabVIEW Graphical Models.

Evolution of Bad Smells in LabVIEW Graphical Models.

SLNET: A Redistributable Corpus of 3rd-party Simulink Models

MuMonDE: A framework for evaluating model clone detectors using model mutation analysis

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