However, little is known about the actual use of these techniques. These works use a variability model to specify the software variability and propose the mapping between the variability model and the software architecture. Dynamic software product lines dspl is a promising approach to enable variability management at runtime. Section 3 presents the kinds of variability found in software processes.
Software product lines enable a family of products to. Many existing works use techniques issued from spls to develop adaptive software 7, 8. Variabilitymodelling practices in industrial software. Variability modeling techniques must thus be able to cope with changes to the variability models. This 2005 report describes the concepts needed when creating core assets with included variability. 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. Variability modeling methods for software product line. Researches in feature modeling can be classified in three main groups based on the technique they used. Semih bilgen september 2009, 4 pages software product lines provide enhanced means for systematic reuse when constructing systems within a particular domain. However, we stopped short of systematically characterizing such domain variability and its effects on requirements.
Systems and software variability management springerlink. Here, we present a list of mutation operators for variability models with annotative variability. 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. Anova was developed by statistician and evolutionary biologist ronald fisher. Modeling domain variability in requirements engineering with.
Tools are provided that support both positive and negative variability on model level. 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. Expressing featurebased variability in structural models. The basis of sple is the explicit modelling of what is common and what differs between product variants. Manifoldness of variability modeling considering the. A conceptual modelling approach to software variability. A study of variability models and languages in the systems.
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. Variability modeling for smart city reference architectures. An important activity in productline development is variability modelling. 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. Variability in software product lines sei digital library. 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. Variability management is achieved through variability modeling, and then using it for decision making. Variability modeling in model driven software product line engineering hassan gomaa1, michael e. Modeling the variability space of selfadaptive applications gilles perrouin, franck chauvel, julien deantoni, jeanmarc jezequel to cite this version. A formal semantics enabling automated, decidable reasoning is given for n ivel by translation to wcrl. Citeseerx problem statement variability models must not. We do this by using variability modeling and modeldriven architecture techniques.
Pdf chapter 2 variability modeling semantic scholar. 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. Modeling variation points is accomplished with two major methods in the context of software product lines. The details about the modeling tools are presented in sect. Using specific modeling techniques can present similar drawbacks. This represents a family of techniques, including lisrel, latent. However, iam systems have process and topology variability, which cause di culties in variability modeling and representation. Statistics, predictive modeling and data mining jmp.
Variability modeling in modeldriven software product line. The anova is based on the law of total variance, where the observed variance in a particular. Classifying variability modeling techniques citeseerx. Modeling and analyzing variability for mobile information. 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. We present a framework and related tool suite for modeling and managing the variability of web servicebased systems for design and runtime, respectively. Varialbd proposes a general metamodel 5, 6 for modeling variability in dbs inherent to every spl. Several frameworks for evaluating, comparing, or classifying feature or variability modeling methods have been suggested. 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.
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. Understanding decisionoriented variability modelling. Evaluating variability modeling techniques for supporting cyber. Modeling variability in the context of software productlines has been around for about 25 years in the research community. Permission to make digital or hard copies of all or part of this work for. We use graphical and formal location modeling techniques, show how to elicit and use location model in conjunction with tropos goaloriented framework. With an increasing number of variants, maintenance can become challenging and organizations may consider migrating towards a software product line, which is referred to as extractive approach. 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. 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.
Then, we refine the modelization to manage the variability in a lower level. A survey of variability modeling in industrial practice. Deelstra 2007 classifying variability modeling techniques ist 76 m. 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. When designing, constructing, and maintaining diverse and variable software systems, a key challenge is the complexity of systems. Evaluating variability modeling techniques for dynamic software. Both kinds of modeling techniques are based on observing and accumulating failure data and analyzing with statistical inference. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Multivariability modeling and realization for software. 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. Visualization of variability and configuration options. By variability we mean the ability of a software system to be extended, changed, customized or con. 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. There are different variability modeling techniques such as feature modeling, functionmeans trees, and configurable components in addition to some other approaches.
While this is the most common approach in practice, techniques to extract variability from cloned variants still fall short in several regards. It is needed to provide a way to systematically organize the variabilities of a group of software systems. In section 1, we indicated the explicit representation of variability of software product families as a key aspect of variability management. N2 in software productline development spld, variability management plays an essential role, and multiple variability modeling methods have been proposed. Variability model implementation on key performance. Software product line engineering with feature models. Static variability modeling in single systems, a class is categorized by the role it plays. 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. Classifying variability modeling techniques sciencedirect. In this section, we discuss variability in terms of variation points and variability dependencies, and present requirements on variability modeling techniques. Proceedings of the seventh international workshop on variability modelling of softwareintensive systems, 23.
Feature modeling is the most famous technique for this purpose 1, 2. A comparison of variability modeling approaches tu graz. Modeling and managing the variability of web servicebased. The perfect enterprise wide solution to visualise, analyse, model, test and maintain all of your systems, software, processes and architectures. T1 variability modeling methods for software product line development. A common approach is variability modeling creating and maintaining models that abstract over the variabilities inherent in such systems. Jul 31, 20 our study 1 supports variability modeling research with empirical data on the realworld use of its flagship concepts. Variability model an overview sciencedirect topics. Variability modeling is a core activity of software product line engineering. We have defined the feature assembly modelling, a feature modelling technique that allows to model variability in software adopting a multi perspective. A key asset in variability management is a variability model, which explicitly specifies the commonalities.
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. Along with gradual acceptance of spld and diversifications of the usage of variability models, there have. The method, developed by the software produc tivity consortium for industrial use, provided an early reuse process model. A potential approach to tackle this challenge are techniques from variability management and product line engineering to handle the diversity and variability. 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. 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. Variability modeling and qos analysis of web services. 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. Modeling and analyzing variability for mobile information systems. The variability modeling experiment by oystein haugen. At this level, there is a passage from a source model to a target model that manages variability.
In software productline development spld, variability management plays an essential role, and multiple variability modeling methods have been proposed. In 18, a product model is obtained by composition and re. 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. The products of an spl can be any kinds of software systems such as embedded systems, software products or digital services. Feature models kan90, cza00 are frequently used for this. 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. 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. We present a framework and related tool suite for modeling and managing the variability of web ser. Exponential increases in complexity and scale make variability a growing threat to sustaining hpc performance at exascale. It started with feature modeling and soon enough was extended to handle many different concerns. Many variability modeling techniques have been proposed and used in academia and practice, for example feature modeling, decision modeling, and orthogonal variability modeling 8.
Application classes are classified according to their role in the application using stereotypes, such as entity class, control class, or interface class. Variability management is a recurrent issue in systems engineering. Systems and software variability management concepts, tools. A framework for modeling variability in software product families splc. The basics of dovml modelling the variability of software systems involves modelling the problem space i. In this paper, we model the variability in service con. From feature modeling to context variability modeling deepdyve. With regression test selection techniques the effects of variability in one dimension may be mapped. Citeseerx questionnairebased variability modeling for.
Index terms variability model, key performance indicator, measurement software, design patter, software quality management. Unlike the other multivariate techniques discussed, structural equation modeling sem examines multiple relationships between sets of variables simultaneously. Variability modeling is important for managing variability in software product families, especially during product derivation. In the case of negative variability, models are tailored based on the absence of features defined in a configuration model.
In the past few years, several variability modeling techniques have. Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. Comparison of variability modeling techniques mini projects. Covamof a framework for modeling variability in software.
Classifying variability modeling techniques information and. This paper concerns modeling and analysis of the complementary relation between software and location variability. By combining different models or analyzing multiple samples, data scientists and other data analysts can reduce the. You must define the desired artifacts for the system definition, the audience for the artifacts, and how the artifact will be used. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling.
Variability modelling for modeldriven development of. In 56, the comprehensibility of two orthogonal variability modeling methods. The goal of this article is precisely to report on the use of variability techniques in the video domain and in an industrial. Variability moves from mechanics and hardware to software, design decisions are delayed as long as economically feasible. Comprehensibility of orthogonal variability modeling. Classifying variability modeling techniques information. Tools are available for download, purchase from various vendors, and even developed inhouse. Three cases of featurebased variability modeling in industry. Modeling the variability space of selfadaptive applications. A development process based on variability modeling for.
Software modeling techniques can be divided into two subcategories. Oct 26, 2008 modeling techniques usually comes down to artifacts diagrams. Variability modeling in software product lines kag. To that end, in this paper, we propose a coherent process for exploring domaincontextual variability and for modeling. Performance variability in hpc io is common, acute, and formidable. Pdf modelling and managing variability with feature assembly. Comparison of variability modeling techniques cse mini. In the past few years, several variability modeling techniques have be. Several techniques are used to model domain space and solution space. 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. Modeling variability in software product lines with the. Sple also includes the design and management of a variable software architecture and its constituent software components.
Gilles perrouin, franck chauvel, julien deantoni, jeanmarc jezequel. 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. It allows you to solve problems, reveal opportunities and make informed decisions in the face of uncertainty. Approach for modeling and implementing variability.
Uml modeling tools for business, software, systems and. Second, there are studies of the sr method itself zhang and ali babar, 20, including synthesis methods cruzes and dyba, 2011, experiences hanssen. 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. Firstclass variability modeling in matlabsimulink in. The feature modeling extension is strictly speaking a workaround to introduce feature diagrams as proposed in 2, 6, 8 into uml. Modeling design variability in software product lines.
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