Identifying Instances of Model Design Patterns and Antipatterns Using Model Clone Detection

Stephan, Matthew; Cordy, James R.

doi:10.1109/mise.2015.16

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…Specifically, DebCheck finds near-miss C function clones that cross between a given application's code and the Gnu-licensed Debian source distribution. This same idea can be applied to model clones, as we have proposed [10].…”

Section: B Model Clone Detectionmentioning

confidence: 99%

“…In addition, we highlight the novelty of our work by comparing it with existing pattern instance detection and analysis techniques. Much of this section is paraphrased and extended from the initial short paper in which we proposed the model clone-based technique [10].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Recently, we argued that instances of model-based patterns can be detected in model-based software using model clone detection [10]. Existing non-model-based software pattern detection can be onerous, requiring those analyzing systems to switch abstraction levels between text and abstract pattern representations, and delaying pattern instance identification to later in the engineering process.…”

mentioning

confidence: 99%

“…Using model clone detection [11], which is a form of model comparison [12], to locate instances of model design patterns and antipatterns (1) can be done early in the Software Engineering process, (2) is applicable to systems that are comprised fully or mostly of modelbased software, and (3) keeps the abstraction level consistent between the patterns and the systems under investigation. This paper builds upon our initial and general ideas of using model clone detection to accomplish model pattern instance detection [10]. In that previous work, we discussed various model clone detection techniques' suitability for detecting pattern instances.…”

mentioning

confidence: 99%

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Identification of Simulink model antipattern instances using model clone detection

Stephan

Cordy

2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Abstract-One challenge facing the Model-Driven Engineering community is the need for model quality assurance. Specifically, there should be better facilities for analyzing models automatically. One measure of quality is the presence or absence of good and bad properties, such as patterns and antipatterns, respectively. We elaborate on and validate our earlier idea of detecting patterns in model-based systems using model clone detection by devising a Simulink antipattern instance detector. We chose Simulink because it is prevalent in industry, has mature model clone detection techniques, and interests our industrial partners. We demonstrate our technique using near-miss crossclone detection to find instances of Simulink antipatterns derived from the literature in four sets of public Simulink projects. We present our detection results, highlight interesting examples, and discuss potential improvements to our approach. We hope this work provides a first step in helping practitioners improve Simulink model quality and further research in the area.I. INTRODUCTION While Model-Driven Engineering (MDE) is becoming increasingly prevalent in the Software Engineering community, especially in both business and embedded domains [1], there are still numerous areas that need to be addressed as both model-based development and systems mature. In MDE, higher-level abstractions, or models, are the primary artifacts in all phases of the Software Engineering life cycle, including requirements, design, implementation, testing, and maintenance. Given the importance and longevity of these models, ascertaining and improving quality of the models and other artifacts of interest in MDE projects, known as model quality assurance, becomes imperative [2], [3]. Compared to quality assurance (QA) for more traditional software development paradigms such as code-based development, model quality assurance is much less refined and researched [4]. If MDE is to continue to flourish and allow engineers to fully reap all of its rewards, then evaluation and improvement of model quality is essential.One method of assessing the quality of Software Engineering systems is to identify and report well-established "good" and "bad" ways of solving specific design questions and constructing the systems' artifacts. These are known as design patterns [5] and antipatterns [6], respectively, and will be referred to collectively simply as "patterns" herein when we are referring to both. Detection of instances of these patterns provides analysts the facility to identify functional and nonfunctional properties that can be used to reason about the

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Section: B Model Clone Detectionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Identification of Simulink model antipattern instances using model clone detection

Stephan

Cordy

2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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“…Similar model sections can be indicative of the reuse of model sections, either intentionally or unintentionally, which can provide developers with the opportunity to create new library blocks, which can replace the clone instances. Model clones can also uncover instances of documented design patterns or anti-patterns [11], assisting in overall quality improvement.…”

Section: Introductionmentioning

confidence: 99%

SimNav: Simulink navigation of model clone classes

Rapos

Stevenson

Alalfi

et al. 2015

2015 IEEE 15th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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Abstract-SimNav is a GUI designed for displaying and navigating clone classes of Simulink models detected by the model clone detector Simone. As an embedded Simulink interface tool, SimNav allows model developers to explore detected clones directly in their own model development environment rather than a separate research tool interface. SimNav allows users to open selected models for side-by-side comparison, in order to visually explore clone classes and view the differences in the clone instances, as well as to explore the context in which the clones exist. This tool paper describes the motivation, implementation, and use cases for SimNav.SimNav can be downloaded here:

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

Stephan

Cordy

2018

Software Testing Verif & Rel

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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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Identifying Instances of Model Design Patterns and Antipatterns Using Model Clone Detection

Cited by 10 publications

References 22 publications

Identification of Simulink model antipattern instances using model clone detection

Identification of Simulink model antipattern instances using model clone detection

SimNav: Simulink navigation of model clone classes

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

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