On Extracting Feature Models from Sets of Valid Feature Combinations

Haslinger, Evelyn Nicole; Lopez-Herrejon, Roberto E.; Egyed, Alexander

doi:10.1007/978-3-642-37057-1_5

Cited by 38 publications

(54 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example it is then possible to derive all corresponding products (e.g., programs) for testing, model-checking, debugging, or measuring performance of them individually. A configuration set can also be used as a benchmark for approaches seeking to synthesize feature models from configurations [3,11,17,23]. As a sound and complete representation of the configurations, the set can act as a ground truth for testing some automated operations [4], etc.…”

Section: Problem Statementmentioning

confidence: 99%

Exploiting the enumeration of all feature model configurations

Galindo

Acher

Tirado³

et al. 2016

Proceedings of the 20th International Systems and Software Product Line Conference

View full text Add to dashboard Cite

Feature models are widely used to encode the configurations of a software product line in terms of mandatory, optional and exclusive features as well as propositional constraints over the features. Numerous computationally expensive procedures have been developed to model check, test, configure, debug, or compute relevant information of feature models. In this paper we explore the possible improvement of relying on the enumeration of all configurations when performing automated analysis operations. The key idea is to precompile configurations so that reasoning operations (queries and transformations) can then be performed in polytime. We tackle the challenge of how to scale the existing enumeration techniques. We show that the use of distributed computing techniques might offer practical solutions to previously unsolvable problems and opens new perspectives for the automated analysis of software product lines.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Exploiting the enumeration of all feature model configurations

Galindo

Acher

Tirado³

et al. 2016

Proceedings of the 20th International Systems and Software Product Line Conference

View full text Add to dashboard Cite

show abstract

“…It works by identifying occurrence patterns in the selected and not selected features that are mapped to parent-child relations of feature models. This work has been extended to consider requires and excludes CTCs (Haslinger et al, 2013); however, it does not support more general types of CTCs. The main distinction with our work is that only one feature model can be reversed engineered, whereas in our approach we could provide different feature models (if they exist) as alternatives for the designers to choose from.…”

Section: Related Workmentioning

confidence: 99%

“…A crucial requirement is capturing all the feature combinations present in SPLs and represent them with feature models (FMs) (Czarnecki and Eisenecker, 2000;Kang et al, 1990), a de facto standard for modelling variability -the capacity of software artefacts to change (Svahnberg et al, 2005). Previous research has addressed this reverse engineering challenge from different perspectives with different approaches such as configuration scripts (She et al, 2011), propositional logic expressions (Czarnecki and Wasowski, 2007), natural language (Weston et al, 2009), and ad hoc algorithms (Haslinger et al, 2011(Haslinger et al, , 2013Acher et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

An assessment of search-based techniques for reverse engineering feature models

Lopez-Herrejon

Linsbauer

Galindo

et al. 2015

Journal of Systems and Software

Self Cite

View full text Add to dashboard Cite

Successful software evolves from a single system by adding and changing functionality to keep up with users' demands and to cater to their similar and different requirements. Nowadays it is a common practice to offer a system in many variants such as community, professional, or academic editions. Each variant provides different functionality described in terms of features. Software Product Line Engineering (SPLE) is an effective software development paradigm for this scenario. At the core of SPLE is variability modelling whose goal is to represent the combinations of features that distinguish the system variants using feature models, the de facto standard for such task. As SPLE practices are becoming more pervasive, reverse engineering feature models from the feature descriptions of each individual variant has become an active research subject. In this paper we evaluated, for this reverse engineering task, three standard search based techniques (evolutionary algorithms, hill climbing, and random search) with two objective functions on 74 SPLs. We compared their performance using precision and recall, and found a clear trade-off between these two metrics which we further reified into a third objective function based on F β , an information retrieval measure, that showed a clear performance improvement. We believe that this work sheds light on the great potential of search-based techniques for SPLE tasks.

show abstract

“…Several techniques for synthesising an FM from a set of configurations or constraints (e.g., encoded as a propositional formula) have been proposed [19,33,6,1,24]. These techniques cannot be applied in our context, since we cannot assume the availability of formal and complete descriptions of configurations or constraints.…”

Section: Related Workmentioning

confidence: 99%

Feature model extraction from large collections of informal product descriptions

Davril

Delfosse

Hariri

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

114

106

View full text Add to dashboard Cite

Feature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.

show abstract

On Extracting Feature Models from Sets of Valid Feature Combinations

Cited by 38 publications

References 12 publications

Exploiting the enumeration of all feature model configurations

Exploiting the enumeration of all feature model configurations

An assessment of search-based techniques for reverse engineering feature models

Feature model extraction from large collections of informal product descriptions

Contact Info

Product

Resources

About