Interface variability in family model mining

Willé, David; Holthusen, Sönke; Schulze, Sandro; Schaefer, Ina

doi:10.1145/2499777.2500708

Cited by 39 publications

(13 citation statements)

References 8 publications

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“…This vastly improves the commonly used clone-and-own practice in mechanical engineering, which introduces lots of redundancies and is error-prone for debugging and maintenance. Wille et al (2013) apply this idea to block-based diagrams, e.g., as available in Matlab/Simulink. Holthusen et al (2014) apply it in a prototypical manner to the IEC 61131-3-language FBD.…”

Section: Evolution Of Variability In Solution Space Artifactsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

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253

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a b s t r a c tCoping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

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Section: Evolution Of Variability In Solution Space Artifactsmentioning

confidence: 99%

Evolution of software in automated production systems: Challenges and research directions

Vogel‐Heuser

Fay

Schaefer

et al. 2015

Journal of Systems and Software

Self Cite

253

View full text Add to dashboard Cite

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“…In [107], the approach is refined to automatically formalize the feature realizations of new product models that are added to the system. In [108] the authors present an approach to mine family models from blockbased models. The similarity between models is measured following an exchangeable metric, taking into account different attributes of the models and can be finetuned depending on the application.…”

Section: Mechanical Comparisonsmentioning

confidence: 99%

Automating the variability formalization of a model family by means of common variability language

Font

Ballarín

Haugen

et al. 2015

Proceedings of the 19th International Conference on Software Product Line

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INTRODUCTION Research Questions related to these challenges: Research Question 1: How to identify and formalize the variability present among a set of product models in terms of features realized by model fragments? Research Question 2: How to capitalize on expert domain knowledge to boost the process of feature location? 1.7. Structure of the dissertation 1.7 Structure of the dissertation This dissertation is structured into five parts: Part I The first part is the introduction of the dissertation, later it presents some background and discusses the state of the art.

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“…In [10], we present an approach to apply family mining to a simple architecture description language (ADL). This approach allows to compare different block-based models with differing interfaces (i.e., the compared blocks can have a differing number of in-and outports), in order to generate family models.…”

Section: The Famine Approachmentioning

confidence: 99%

“…The base model is defined to be the model with the smallest number of blocks. The metric assigns different weights to the properties of the blocks, such as block type or the number of in-and outports, and is used during the comparison of blocks in order to calculate their similarity [10]. We start the comparison of the models by comparing all start blocks from the base model with all start blocks from the compare model.…”

Section: The Famine Approachmentioning

confidence: 99%

Managing lots of models: the FaMine approach

Willé

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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In this paper we present recent developments in reverse engineering variability for block-based data-flow models. MOTIVATIONModel-based languages, such as MATLAB/Simulink, AS-CET, or SCADE, are common means to model continuous data flow between different components of systems (e.g., between control units). Model-driven development with these languages is often applied for development in domains with high complexity (e.g., the automotive sector) [2]. The functionality of the developed components is represented by atomic function blocks, provided by the languages' libraries. These blocks can exchange data via connections between their inand outports, which also define the block interfaces. Most model-based languages also provide hierarchic blocks, which allow to decompose a model into different hierarchy levels. By using these blocks, the development starts at a very basic level and the created solution is refined with each added hierarchy level. This approach reduces the complexity further, because problems can be handled in a stepwise manner.As the development of complex systems is a tedious and expensive task, new models with similar, but differing functionality are often created by copying an existing model and modifying it to satisfy changed requirements [2,3]. This socalled clone-and-own approach includes deletions, additions and modifications of blocks, interfaces, and connections. As an increasing number of related models evolve from the existing software system, several issues arise. For example, testing the growing number of model variants is very expensive, because common parts are tested repeatedly. Applying bug fixes becomes a tedious task, since developers need to check each of the model variants to fix the error for the whole model family. In addition, updating the different variants to new versions turns into a complex and expensive task, because the different variants might have been modified when they were created from the initial model, such that updates might not be directly applicable. Because of these issues, it is crucial to identify the commonalities and differences between models and store this information in a way that developers understand and can easily use for their work.Family mining analyzes the created model family to identify and store the information about the variability between its product variants. The contribution of the FaMine approach is the automatic analysis of commonalities and differences of related models. The obtained information allows us to create family models, which store the information about the commonalities and differences of the software family. This information allows developers to get a better understanding of the variability between the compared models, which helps with many development tasks, such as testing, debugging, and maintenance. BACKGROUNDCzarnecki et al. distinguish between problem space and solution space of software families [1]. The problem space specifies features, that can be used to solve a specific problem without giving any implementation d...

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Interface variability in family model mining

Cited by 39 publications

References 8 publications

Evolution of software in automated production systems: Challenges and research directions

Evolution of software in automated production systems: Challenges and research directions

Automating the variability formalization of a model family by means of common variability language

Managing lots of models: the FaMine approach

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