Clone detection in automotive model-based development

2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering

2015

Abstract-A hurdle in the growth of model driven software engineering is our ability to evaluate the quality of models automatically. One perspective is that software quality is a function of the existence, or lack thereof, of good and bad properties, also known as patterns and antipatterns, respectively. In this paper, we introduce the notion of using model clone detection to detect model pattern and antipattern instances by looking for models that are cross clones of pattern models. By detecting patterns at the model level, analysis is accomplished earlier in the engineering process, can be applied to primarily modelbased projects, and remains at the same level of abstraction that engineers are used to. We outline the process of using model clone detection for this purpose, including representing the patterns and detection of instances. We present some Simulink examples of pattern representations and discuss future work and research in the area. I. INTRODUCTIONModel-driven Engineering (MDE) is a relatively new approach to software development that entails higher-level abstractions, or models, being used as the primary artifacts in system development and management. This includes incorporating modeling in all four phases of Software Engineering: requirements, design, implementation, and testing. By utilizing artifacts at an abstraction level closer to the problem space and hiding lower-level details, MDE can yield higher quality systems, facilitate better communication among stakeholders, and make project teams adaptable and flexible. MDE has seen significant levels of adoption in many different domains, including aviation, automotive, aerospace, and other highreliability embedded systems applications.While MDE use is growing, and engineers and customers are starting to experience the benefits of using model-based techniques [1], there are still many obstacles to overcome. One example is model quality evaluation, that is, the ability to assess quality of the models and artifacts of interest across the MDE life cycle [2], [3]. When it comes to more traditional software engineering processes, such as those that center around third generation programming languages, quality assurance is a well-researched and established area. In contrast, much less is understood about quality assurance for models in the MDE context. Improving our knowledge and making it easier to reason about model quality is a necessary step in continuing the growth of MDE.One approach to assessing software quality involves detecting the existence of design patterns [4] and antipatterns [5] in

Section: Background and Related Work A Model Comparison And Modementioning

confidence: 99%

“…In this paper we argue that detection of pattern and antipattern instances in models can be achieved using a specialized form of model comparison [9] known as model clone detection [10]. Model comparison involves comparing models and explicating both similarities and differences.…”

Section: Introductionmentioning

confidence: 99%

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

2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering

2015

“…Model clone detection refers specifically to the identification of clones within model-based software systems and has been implemented in a variety of ways for different modelling languages [4], [7], [8]. It is generally accepted that there are three types of model clones [4]: …”

Section: B Model Clone Detectionmentioning

confidence: 99%

“…Changing a type is relatively significant and essential to a block's identity. For example, model-clone detectors, including Simone and CloneDetective [7], consider blocks with different types to be non-equivalent. As such, it is clear that there is a need for a class of mutations that account for this.…”

Section: ) Add or Delete Block As Destination (Madbd)mentioning

confidence: 99%

“…Through the use and creation of block libraries and sublibraries 7 , extracting and using subsystems in this manner is very plausible. From a model-clone detection perspective, this mutation class is important, because it will help test whether or not a model clone detector can properly account for subsystem boundaries.…”

Section: ) Changing a Subsystem's Clone Hierarchy (Mcsch)mentioning

confidence: 99%

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Towards a Taxonomy for Simulink Model Mutations

Alalfi

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

2014

Abstract-A relatively new and important branch of Mutation Analysis involves model mutations. In our attempts to realize model-clone detector testing, we found that there was little mutation research on Simulink, which is a fairly prevalent modeling language, especially in embedded domains. Because Simulink model mutations are the crux of our model-clone detector testing framework, we want to ensure that we are selecting the appropriate mutations. In this paper, we propose a taxonomy of Simulink model mutations, which is based on our experiences thus far with Simulink, that aims to inject model clones of various types and is fairly representative of realistic Simulink edit operations. We organize the mutations by categories based on the types of model clones they will inject, and further break them down into mutation classes. For each class, we define the characteristics of mutation operators belonging to that class and demonstrate an example operator. Lastly, in an attempt to validate our taxonomy, we perform a case study on multiple versions of three Simulink projects, including an industrial project, to ascertain if the actual subsystem edit operations observed across versions can be classified using our taxonomy and present any interesting cases. While we developed the taxonomy with the specific goal of facilitating and guiding the injection of mutants for model clones, we believe it is fairly general and a solid foundation for future Simulink model mutation work.

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

Software Testing Verif & Rel

2018

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.