Efficient synthesis of feature models

Andersen, Nele; Czarnecki, Krzysztof; She, Steven; Wąsowski, Andrzej

doi:10.1145/2362536.2362553

Cited by 50 publications

(63 citation statements)

References 36 publications

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“…The same observation applies for the slicing operation so that reasoning directly at the semantic level is required. The key ideas of our approach are to i) compute the propositional formula representing the projected set of configurations and then ii) reuse the reasoning techniques proposed in [21,11,8] to construct an FM from the propositional formula. We rely on the algorithm developed in [5] that combines this information with the known hierarchy of the slice (see Definition 6) in order to build a complete and valid FM.…”

Section: Automationmentioning

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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Section: Automationmentioning

confidence: 99%

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

et al. 2013

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“…Andersen et. al propose algorithms to reverse engineer feature models from propositional logic formulas, these formulas are either in disjunctive normal form (DNF) or conjunctive normal form (CNF) [16]. Note here that while an FST can be viewed as a propositional logic formula in DNF, our approach is very different from Andersen et.…”

Section: Related Workmentioning

confidence: 99%

On Extracting Feature Models from Sets of Valid Feature Combinations

Haslinger

Lopez-Herrejon

Egyed

2013

Fundamental Approaches to Software Engineering

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Abstract. Rather than developing individual systems, Software Product Line Engineering develops families of systems. The members of the software family are distinguished by the features they implement and Feature Models (FMs) are the de facto standard for defining which feature combinations are considered valid members. This paper presents an algorithm to automatically extract a feature model from a set of valid feature combinations, an essential development step when companies, for instance, decide to convert their existing product variations portfolio into a Software Product Line. We performed an evaluation on 168 publicly available feature models, with 9 to 38 features and up to 147456 feature combinations. From the generated feature combinations of each of these examples, we reverse engineered an equivalent feature model with a median performance in the low milliseconds.

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“…Second we compute a directed minimum spanning tree (MST) of BIG c that maximises the parent-child relationships of input FM hierarchies. Finally, other components of the feature diagrams can be synthesized [19,32]. In Fig.…”

Section: Denotational-based Composition (Logic-based)mentioning

confidence: 99%

“…For instance, the operational-based composition has the worst maximality since the resulting feature diagram is a super-set of all combinations of features and is a very rough over-approximation of s. In particular, the feature F2 is optional in the feature diagram whereas it is always included in every configuration. The other variants have the best possible maximality since they all rely on the logical synthesis technique that is known to produce a maximal feature diagram [32].…”

Section: Comparison Framework and Reading Gridmentioning

confidence: 99%

Composing Your Compositions of Variability Models

Acher

Combemale

Collet

et al. 2013

Lecture Notes in Computer Science

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Abstract. Modeling and managing variability is a key activity in a growing number of software engineering contexts. Support for composing variability models is arising in many engineering scenarios, for instance, when several subsystems or modeling artifacts, each coming with their own variability and possibly developed by different stakeholders, should be combined together. In this paper, we consider the problem of composing feature models (FMs), a widely used formalism for representing and reasoning about a set of variability choices. We show that several composition operators can actually be defined, depending on both matching/merging strategies and semantic properties expected in the composed FM. We present four alternative forms and their implementations. We discuss their relative trade-offs w.r.t. reasoning, customizability, traceability, composability and quality of the resulting feature diagram. We summarize these findings in a reading grid which is validated by revisiting some relevant existing works. Our contribution should assist developers in choosing and implementing the right composition operators.

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Efficient synthesis of feature models

Cited by 50 publications

References 36 publications

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

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

On Extracting Feature Models from Sets of Valid Feature Combinations

Composing Your Compositions of Variability Models

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