Consistency maintenance for evolving feature models

Guo, Jianmei; Wang, Yinglin; Trinidad, Pablo; Benavides, David

doi:10.1016/j.eswa.2011.10.014

Cited by 35 publications

(32 citation statements)

References 30 publications

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“…Then, one will have to adapt the feature model comparison process to support that new classification. Previous work on feature models showed that their maintenance can be complex and error prone [5,15]. With an approach such as FMDiff, it would be possible to extract new information about the evolution of the features using already existing artefacts, at the cost of adapting our tool.…”

Section: Threats To Validitymentioning

confidence: 99%

“…Feature model evolution has been extensively studied in the past [15,26,41,44]. These studies provide insights on which operations may occur on features, detailed examples of transformations occurring on large scale product linesindustrial and open source, and the evolution of feature model structural metrics (number of leaves, nodes, constraints).…”

Section: Introductionmentioning

confidence: 99%

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Analysing the Linux kernel feature model changes using FMDiff

2015

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Evolving a large scale, highly variable system is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine-grained feature changes can be used to guide the evolution of the highly variable system. In this paper, we present an approach to obtain fine-grained feature model changes with its supporting tool "FMDiff". Our approach is tailored for Kconfig-based variability models and proposes a feature change classification detailing changes in features, their attributes and attribute values. We apply our approach to the Linux kernel feature model, extracting feature changes occurring in sixteen official releases. In contrast to previous studies, we found that feature modifications are responsible for most of the changes. Then, by taking advantage of the multi-platform aspect of the Linux kernel, we observe the effects of a feature change across the different architecture-specific feature models of the kernel. We found that between 10 and 50 % of feature changes impact all the architecture-specific feature

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Section: Threats To Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysing the Linux kernel feature model changes using FMDiff

2015

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“…While the evolution of Boolean fms as been widely studied in the literature [12,1,16,14,10,28,19,26,18,17], little is known about cardinality-based fm evolution. For instance, cloud environments evolve over time, e.g., when a new service support is available or the provided amount of resources has been extended.…”

Section: Motivating Examplementioning

confidence: 99%

“…Several approaches address fm analysis [2], e.g., to detect inconsistencies that can arise during specification and evolution of fms. Other work discusses edits (changes) on fms during evolution, e.g., with respect to the resulting set of products [28], the mappings to solution space [26] or fm consistency [10].…”

Section: Introductionmentioning

confidence: 99%

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Consistency checking for the evolution of cardinality-based feature models

Quinton

Pleuss

Berre

et al. 2014

Proceedings of the 18th International Software Product Line Conference - Volume 1

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Toward automated quality-centric product line configuration using intentional variability

Noorian

Bagheri

2017

J Softw Evol Proc

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Software product line engineering is a discipline that facilitates a systematic reuse-based approach by formally representing commonalities and variabilities between the applications of a target domain. As one of the main artifacts of the software product line, a feature model represents the possible configuration space and can be customized based on the stakeholders' needs.Considering the complexity of the variabilities represented by feature models and the diversity of the stakeholders' expectations, the configuration process can be viewed as a complex optimization problem. In previous research, researchers have bridged the gap between requirement and product line engineering by integrating feature models and goal models. In this paper, we propose an approach for the configuration process that seeks to satisfy the stakeholders' requirements as well as the feature models' structural and integrity constraints. We model stakeholders' functional and nonfunctional needs and their preferences using requirement engineering goal models. We formalize the structure of the feature model, the stakeholders' objectives, and their preferences in the form of an integer linear program to conduct a semi-automated feature model configuration process. Our experimental results show that the proposed configuration framework is scalable when considering both functional and nonfunctional requirements of stakeholders.

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Consistency maintenance for evolving feature models

Cited by 35 publications

References 30 publications

Analysing the Linux kernel feature model changes using FMDiff

Analysing the Linux kernel feature model changes using FMDiff

Consistency checking for the evolution of cardinality-based feature models

Toward automated quality-centric product line configuration using intentional variability

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