Structuring the modeling space and supporting evolution in software product line engineering

Dhungana, Deepak; Grünbacher, Paul; Rabiser, Rick; Neumayer, Thomas

doi:10.1016/j.jss.2010.02.018

Cited by 85 publications

(55 citation statements)

References 50 publications

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“…Van Ommering [61] introduces the concept of Product Populations. Dhungana et al [62] explore how to structure the modeling space for large product lines at multiple levels of abstraction. Holl et al [63] describe the concept of product line bundles to package variability models and product line tool extensions to support sharing and deploying models and tools in multi product lines.…”

Section: Related Workmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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Context: In industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique-they will rather use different approaches and tools to manage the variability of their systems. Objective: We aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations. Method: We present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services. Results: We show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach. Conclusions: Tools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.

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Section: Related Workmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

Self Cite

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“…In [22,53], Dhungana et al report that evolution support becomes particularly important for engineering SPLs and other variabilityintensive systems. They propose model-driven support at the feature level, using FM concepts [53].…”

Section: Evolution Of Architectural Variabilitymentioning

confidence: 99%

Extraction and evolution of architectural variability models in plugin-based systems

et al. 2013

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Variability management is a key issue when building and evolving software-intensive systems, making it possible to extend, configure, customize and adapt such systems to customers' needs and specific deployment contexts. A wide form of variability can be found in extensible software systems, typically built on top of plugin-based architectures that offer a (large) number of configuration options through plugins. In an ideal world, a software architect should be able to generate a system variant on-demand, corresponding to a particular assembly of plugins. To this end, the variation points and constraints between architectural elements should be properly modeled and maintained over time (i.e., for each version of an architecture). A crucial, yet errorprone and time-consuming, task for a software architect is to build an accurate representation of the variability of an architecture, in order to prevent unsafe architectural variants and reach the highest possible level of flexibility. In this article, we propose a reverse engineering process for producing a variability model (i.e., a feature model) of a plugin-based architecture. We develop automated techniques to extract and combine different variability descriptions, including a hierarchical software architecture model, a plugin dependency model and the software architect knowledge. By computing and reasoning about differences between versions of architectural feature models, software architect can control both the variability extraction and evolution processes. The proSend offprint requests to: posed approach has been applied to a representative, large-scale plugin-based system (FraSCAti), considering different versions of its architecture. We report on our experience in this context.

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“…Managing evolution however is success-critical, especially in modelbased product line approaches to ensure consistency after changes to meta-models, models, and actual development artifacts. Some approaches provide explicit support for particular aspects of product line evolution [67,106,64,165,161,138,199,119].…”

Section: Evolution Of Diverse Systemsmentioning

confidence: 99%

Software diversity: state of the art and perspectives

Schaefer

Rabiser²,

Clarke

et al. 2012

Int J Softw Tools Technol Transfer

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137

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Abstract. Diversity is prevalent in modern software systems to facilitate adapting the software to customer requirements or the execution environment. Diversity has an impact on all phases of the software development process. Appropriate means and organizational structures are required to deal with the additional complexity introduced by software variability. This introductory article to the special section "Software Diversity -Modeling, Analysis and Evolution" provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions. The article covers requirements analysis, design, implementation, verification and validation, maintenance and evolution as well as organizational aspects. It also provides an overview of the articles which are part of this special section and address particular issues of diverse systems development.

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Structuring the modeling space and supporting evolution in software product line engineering

Cited by 85 publications

References 50 publications

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Extraction and evolution of architectural variability models in plugin-based systems

Software diversity: state of the art and perspectives

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