Context Aware Reconfiguration in Software Product Lines

Mauro, Jacopo; Nieke, Michael; Seidl, Christoph; Yu, Ingrid Chieh

doi:10.1145/2866614.2866620

Cited by 32 publications

(24 citation statements)

References 18 publications

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“…We will illustrate this in our case studies, as well as the use of each of the above type of quantitative constraints over feature attributes, by providing concrete examples. It is important to note that the above type of quantitative constraints are significantly more complex than the ones that are commonly associated to attributed feature models [23], [24], [25], [26]. We are not aware of any other approach dealing with all of the above aspects in one unifying framework.…”

Section: Modeling With the Flan Familymentioning

confidence: 99%

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A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

Beek

Legay

Lafuente

et al. 2020

IIEEE Trans. Software Eng.

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This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLAN, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLAN specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. Our analysis is based on statistical model checking techniques, which allow us to scale to larger models with respect to precise probabilistic analysis techniques. The analyses we can conduct range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Our approach is supported by a novel Eclipse-based tool which includes state-of-the-art DSL utilities for QFLAN based on the Xtext framework as well as analysis plug-ins to seamlessly run statistical model checking analyses. We provide a number of case studies that have driven and validated the development of our framework. ! arXiv:1707.08411v2 [cs.SE]

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Section: Modeling With the Flan Familymentioning

confidence: 99%

“…Variables allow, e.g., to encode state information or context information (e.g. to model context-aware SPLs [25], [26]).…”

Section: Variablesmentioning

confidence: 99%

A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

Beek

Legay

Lafuente

et al. 2020

IIEEE Trans. Software Eng.

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“…On the deployment side, we introduce a cloud-based tool chain that complements our development tools [5]. The tool chain functions in an automotive context, keeping tabs on a large number of connected devices and their feature configurations.…”

Section: Hyvar Solutionmentioning

confidence: 99%

HyVar

Røst¹,

Seidl

et al. 2018

Communications in Computer and Information Science

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The HyVar project (www.hyvar-project.eu/) proposes a development framework for continuous and individualized evolution of distributed software applications running on remote devices in heterogeneous environments, focusing on the automotive domain. The framework combines variability modeling and software reuse from software product lines with formal methods and software upgrades and can be integrated in existing software development processes. HyVar's objectives are: (O1) To develop a Domain Specific Variability Language (DSVL) and tool chain to support software variability for highly distributed applications; (O2) to develop a cloud infrastructure that exploits software variability as described in the DSVL to track the software configurations deployed on remote devices and to enable (i) the collection of data from the devices to monitor their behavior; and (ii) secure and efficient customized updates; (O3) to develop a technology for over-the-air updates of distributed applications, which enables continuous software evolution after deployment on complex remote devices that incorporate a system of systems; and (O4) to test HyVar's approach as described in the above objectives in an industry-led demonstrator to assess in quantifiable ways its benefits. The end of the project is approaching and we are close to reaching all the objectives. In this paper, we present the integrated tool chain, which combines formal reuse through software product lines with commonly used industrial practices, and supports the development and deployment of individualized software adaptations. We also describe the main benefits for the stakeholders involved.

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“…In DSPL approaches, feature models may be enriched with context [10] and binding time [11], [12], [13] information. A reconfiguration is usually triggered by monitored context features or another system component requesting the inclusion (exclusion) of a set of features to (from) the current configuration.…”

Section: Motivationmentioning

confidence: 99%

Extending Dynamic Software Product Lines with Temporal Constraints

Sousa

Rudametkin²,

Duchien³

2017

2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Due to the number of cloud providers, as well as the extensive collection of services, cloud computing provides very flexible environments, where resources and services can be provisioned and released on demand. However, reconfiguration and adaptation mechanisms in cloud environments are very heterogeneous and often exhibit complex constraints. For example, when reconfiguring a cloud system, a set of available services may be dependent on previous choices, or there may be alternative ways of adapting the system, with different impacts on performance, costs or reconfiguration time. Cloud computing systems exhibit high levels of variability, making dynamic software product lines (DSPLs) a promising approach for managing them. However, in DSPL approaches, verification is often limited to verifying conformance to a variability model, but this is insufficient to verify complex reconfiguration constraints that exist in cloud computing systems. In this paper, we propose the use of temporal constraints and reconfiguration operations to model a DSPL's reconfiguration lifecycle. We demonstrate how these concepts can be used to model the variability of cloud systems, and we use our approach to identify reconfigurations that meet given criteria.

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Context Aware Reconfiguration in Software Product Lines

Cited by 32 publications

References 18 publications

A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

HyVar

Extending Dynamic Software Product Lines with Temporal Constraints

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