Change Impact Analysis of Feature Models

Paškevičius, Paulius; Damaševičius, Robertas; Štuikys, Vytautas

doi:10.1007/978-3-642-33308-8_10

Cited by 20 publications

(13 citation statements)

References 25 publications

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“…While the Linux kernel is not a software product line, it does have the main technical characteristics of such systems [36] hinting that our approach could be applicable in this larger context. Existing feature change classifications [8,26] can be adapted, as we did in this work, to match other feature notations. Then, one will have to adapt the feature model comparison process to support that new classification.…”

Section: Threats To Validitymentioning

confidence: 99%

“…In his thesis, Paskevicius describes [26] several transformations that can be applied to a FM. Similarly, FM change patterns have been identified by Alves et al in [3] and Neves et al in [25].…”

Section: Related Workmentioning

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%

“…We aim at classifying feature changes occurring in the Linux kernel feature model (FM), capturing as accurately as possible the different changes that might occur on its statement. Existing feature change classifications [8,26] do not consider some specificities of the Kconfig grammar (e.g. select relationships with conditions).…”

Section: Change Classificationmentioning

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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Change Classificationmentioning

confidence: 99%

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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%