The details about the modeling tools are presented in sect. Modeling and analyzing variability for mobile information systems. General terms software processes, variability modeling keywords software process lines, model driven engineering, process asset reuse this work has been partly funded by project fondef d09i1171, chile. In this paper, we elaborate on the reference architecture by enabling smartcityra with variability mechanisms to accommodate the instantiations of different smart city software architectures.
Modeling variability in the context of software productlines has been around for about 25 years in the research community. In this paper, we model the variability in service con. Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. Then, we refine the modelization to manage the variability in a lower level. A development process based on variability modeling for. Variability modeling is a core activity of software product line engineering. Variability management is achieved through variability modeling, and then using it for decision making.
In 56, the comprehensibility of two orthogonal variability modeling methods. We have defined the feature assembly modelling, a feature modelling technique that allows to model variability in software adopting a multi perspective. The basis of sple is the explicit modelling of what is common and what differs between product variants. Variability model implementation on key performance. A potential approach to tackle this challenge are techniques from variability management and product line engineering to handle the diversity and variability. It arises for example in enterprise systems, where modules are configured and composed to meet the requirements of individual customers based on modifications to a reference model. However, iam systems have process and topology variability, which cause di culties in variability modeling and representation. Variability modelling for modeldriven development of. Several frameworks for evaluating, comparing, or classifying feature or variability modeling methods have been suggested. The goal of this article is precisely to report on the use of variability techniques in the video domain and in an industrial. Pdf modelling and managing variability with feature assembly.
Analysis of variance anova is a collection of statistical models and their associated estimation procedures such as the variation among and between groups used to analyze the differences among group means in a sample. However, we 2 also provide requirements for concepts and mechanisms that are not commonly considered in academic techniques, and 3 challenge assumptions about size and complexity of variability models made in academic papers. It started with feature modeling and soon enough was extended to handle many different concerns. T1 variability modeling methods for software product line development. The products of an spl can be any kinds of software systems such as embedded systems, software products or digital services. Exponential increases in complexity and scale make variability a growing threat to sustaining hpc performance at exascale. Varialbd proposes a general metamodel 5, 6 for modeling variability in dbs inherent to every spl. Since many variability modeling techniques do not currently support this, research is required to allow an evolution of the distinction between what is variable and what is invariant over time. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Techniques for modeling variability in design include modeling variability using parameterization, modeling variability using information hiding, and modeling variability using inheritance gomaa and webber 2004. A framework for modeling variability in software product families splc.
Modeling and analyzing variability for mobile information. It is needed to provide a way to systematically organize the variabilities of a group of software systems. Variability modeling techniques must thus be able to cope with changes to the variability models. While this is the most common approach in practice, techniques to extract variability from cloned variants still fall short in several regards. In the case of negative variability, models are tailored based on the absence of features defined in a configuration model.
The perfect enterprise wide solution to visualise, analyse, model, test and maintain all of your systems, software, processes and architectures. A survey of variability modeling in industrial practice. Anova was developed by statistician and evolutionary biologist ronald fisher. Citeseerx problem statement variability models must not. You must define the desired artifacts for the system definition, the audience for the artifacts, and how the artifact will be used. In the past few years, several variability modeling techniques have be. By combining different models or analyzing multiple samples, data scientists and other data analysts can reduce the.
Researches in feature modeling can be classified in three main groups based on the technique they used. Understanding decisionoriented variability modelling. Section 3 presents the kinds of variability found in software processes. Feature modeling is the most famous technique for this purpose 1, 2. Statistics, predictive modeling and data mining jmp. Citeseerx questionnairebased variability modeling for. Modeling domain variability in requirements engineering with.
Covamof a framework for modeling variability in software. The anova is based on the law of total variance, where the observed variance in a particular. In this section, we discuss variability in terms of variation points and variability dependencies, and present requirements on variability modeling techniques. From feature modeling to context variability modeling deepdyve. In predictive modeling and other types of data analytics, a single model based on one data sample can have biases, high variability or outright inaccuracies that affect the reliability of its analytical findings. While many variability modeling techniques and notations have been proposed, little is known about industrial practices and how industry values or criticizes this class of modeling. However, little is known about the actual use of these techniques. Software modeling techniques can be divided into two subcategories. With regression test selection techniques the effects of variability in one dimension may be mapped. Permission to make digital or hard copies of all or part of this work for. The aim of this chapter is to provide a comprehensive description of the notion of variability modeling in the context of software product line engineering and to give an overview of the techniques proposed for variability modeling. Both kinds of modeling techniques are based on observing and accumulating failure data and analyzing with statistical inference.
N2 in software productline development spld, variability management plays an essential role, and multiple variability modeling methods have been proposed. Variability modeling for smart city reference architectures. A comparison of variability modeling approaches tu graz. The success of product line engineering techniques in the last 15 years has popularized the use of software variability as a key modeling approach for describing the commonality and variability of systems at all stages of the software lifecycle. Software and systems modeling sosym is an international journal, publishing six issues yearly, that focuses on theoretical and practical issues in the development and application of software and system modeling languages, techniques, and methods, such as the unified modeling language. Firstclass variability modeling in matlabsimulink in. Software product lines enable a family of products to. Performance variability in hpc io is common, acute, and formidable. Modeling design variability in software product lines. Variability modeling in modeldriven software product line. Variability moves from mechanics and hardware to software, design decisions are delayed as long as economically feasible. Systems and software variability management springerlink.
In 18, a product model is obtained by composition and re. These works use a variability model to specify the software variability and propose the mapping between the variability model and the software architecture. However, we stopped short of systematically characterizing such domain variability and its effects on requirements. By variability we mean the ability of a software system to be extended, changed, customized or con gured for use in a speci c context. Variability model an overview sciencedirect topics. Systems and software variability management concepts, tools. Manifoldness of variability modeling considering the. Comparison of variability modeling techniques cse mini.
Variability modeling in model driven software product line engineering hassan gomaa1, michael e. Design techniques include standard modeling languages such as the unified modeling language uml, frameworks such as the model driven architecture mda, and software processes such as the enterprise unified process eup. Mda generic ways to describe variability that can work well with product line descriptions in any base language techniques for making generic tools that can work well with the tools for the base languages. In software productline development spld, variability management plays an essential role, and multiple variability modeling methods have been proposed. We present a framework and related tool suite for modeling and managing the variability of web ser. Modeling the variability space of selfadaptive applications. Variability in software product lines sei digital library. Sple also includes the design and management of a variable software architecture and its constituent software components.
A formal semantics enabling automated, decidable reasoning is given for n ivel by translation to wcrl. A conceptual modelling approach to software variability. Here, we present a list of mutation operators for variability models with annotative variability. Visualization of variability and configuration options. Semih bilgen september 2009, 4 pages software product lines provide enhanced means for systematic reuse when constructing systems within a particular domain. We do this by using variability modeling and modeldriven architecture techniques. Using specific modeling techniques can present similar drawbacks. Gilles perrouin, franck chauvel, julien deantoni, jeanmarc jezequel. Dynamic software product lines dspl is a promising approach to enable variability management at runtime. Implementing variability model to key performance indicator specification process is the focus of this paper, with support tool of key performance indicator specification process provided. To that end, in this paper, we propose a coherent process for exploring domaincontextual variability and for modeling. At this level, there is a passage from a source model to a target model that manages variability. Deelstra 2007 classifying variability modeling techniques ist 76 m. Proceedings of the seventh international workshop on variability modelling of softwareintensive systems, 23.
Many existing works use techniques issued from spls to develop adaptive software 7, 8. We present a framework and related tool suite for modeling and managing the variability of web servicebased systems for design and runtime, respectively. If documentation is the goal, then technology and techniques should work toward delivering structured output, be that in the form of a document or a. The basics of dovml modelling the variability of software systems involves modelling the problem space i. Modeling the variability space of selfadaptive applications gilles perrouin, franck chauvel, julien deantoni, jeanmarc jezequel to cite this version. Variability modeling is important for managing variability in software product families, especially during product derivation.
Variability modeling and qos analysis of web services. We use graphical and formal location modeling techniques, show how to elicit and use location model in conjunction with tropos goaloriented framework. No doubt, that it requires adequate and effective different types of data analysis methods, techniques, and tools that can respond to constantly increasing business research needs. Application classes are classified according to their role in the application using stereotypes, such as entity class, control class, or interface class. It allows you to solve problems, reveal opportunities and make informed decisions in the face of uncertainty. Comparison of variability modeling techniques mini projects. Beyond being used for a mere description and documentation of variability, variability models are more and more leveraged to produce other artifacts, such as configurators. Approach for modeling and implementing variability. Classifying variability modeling techniques sciencedirect. Variability modeling in software product lines kag. In the past few years, several variability modeling techniques have been developed, each using its own concepts to model the variability provided by a product family. Pdf chapter 2 variability modeling semantic scholar. In section 1, we indicated the explicit representation of variability of software product families as a key aspect of variability management. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling.
This represents a family of techniques, including lisrel, latent. A common approach is variability modeling creating and maintaining models that abstract over the variabilities inherent in such systems. Evaluating variability modeling techniques for dynamic software. Tools are available for download, purchase from various vendors, and even developed inhouse. Multivariability modeling and realization for software. Variability modeling methods for software product line. Static variability modeling in single systems, a class is categorized by the role it plays. Modeling variability in software product lines with the. Variability modelling has received tremendous attention in the past few decades and is the basis for both researchbased 1, 3, 4 and industrial productline methodologies 5, 6. Request pdf variability modeling the aim of this chapter is to provide a comprehensive description of the notion of variability modeling in the context of software product line. On this basis, a discussion is presented that concerns the potential and feasibility of integrating all these diverse techniques into a single, common technique for variability modeling. Jul 31, 20 our study 1 supports variability modeling research with empirical data on the realworld use of its flagship concepts. Evaluating variability modeling techniques for supporting cyber. The variability modeling experiment by oystein haugen.
Along with gradual acceptance of spld and diversifications of the usage of variability models, there have. Enterprise architect is the ideal platform to help you to stay in control of your workspace, support your colleagues and team, enable collaboration and build confidence within your most complex. Software product line engineering with feature models. A key asset in variability management is a variability model, which explicitly specifies the commonalities. We take the first step towards comprehensively studying linear and nonlinear approaches to modeling hpc io system variability in an effort to demonstrate that variability is often a predictable. A study of variability models and languages in the systems. Tools are provided that support both positive and negative variability on model level. Uml modeling tools for business, software, systems and. Expressing featurebased variability in structural models. Evaluating variability modeling techniques for supporting. These concepts provide guidelines to core asset creators on how to model the variability explicitly, so it is handled consistently throughout the product line and managing the variability becomes feasible. When designing, constructing, and maintaining diverse and variable software systems, a key challenge is the complexity of systems. Modeling and managing the variability of web servicebased. In the past few years, several variability modeling techniques have.
Unlike the other multivariate techniques discussed, structural equation modeling sem examines multiple relationships between sets of variables simultaneously. This paper concerns modeling and analysis of the complementary relation between software and location variability. Variability management is a recurrent issue in systems engineering. Index terms variability model, key performance indicator, measurement software, design patter, software quality management. Variabilitymodelling practices in industrial software. By variability we mean the ability of a software system to be extended, changed, customized or con. Several techniques are used to model domain space and solution space. The feature modeling extension is strictly speaking a workaround to introduce feature diagrams as proposed in 2, 6, 8 into uml. Modeling variation points is accomplished with two major methods in the context of software product lines. Feature models kan90, cza00 are frequently used for this. Classifying variability modeling techniques information.
There are different variability modeling techniques such as feature modeling, functionmeans trees, and configurable components in addition to some other approaches. An important activity in productline development is variability modelling. Classifying variability modeling techniques citeseerx. Many variability modeling techniques have been proposed and used in academia and practice, for example feature modeling, decision modeling, and orthogonal variability modeling 8.
Oct 26, 2008 modeling techniques usually comes down to artifacts diagrams. Three cases of featurebased variability modeling in industry. The method, developed by the software produc tivity consortium for industrial use, provided an early reuse process model. Comprehensibility of orthogonal variability modeling. Over years of variability modeling, feature modeling using features diagrams was the most popular technique to represent variability in clear and meaningful way 1. Fabian benduhn representing variability in product lines. Classifying variability modeling techniques information and. Investigating styles in variability modeling kathrin figl.
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