Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Detecting and Explaining Conflicts in Attributed Feature Models

Product configuration systems are often based on a variability model. The development of a variability model is a time consuming and error-prone process. Considering the ongoing development of products, the variability model has to be adapted frequently. These changes often lead to mistakes, such that some products cannot be derived from the model anymore, that undesired products are derivable or that there are contradictions in the variability model. In this paper, we propose an approach to discover and to explain contradictions in attributed feature models efficiently in order to assist the developer with the correction of mistakes. We use extended feature models with attributes and arithmetic constraints, translate them into a constraint satisfaction problem and explore those for contradictions. When a contradiction is found, the constraints are searched for a set of contradicting relations by the QuickXplain algorithm.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301

Analysis of Feature Models using Generalised Feature Trees

This paper introduces the concept of generalised feature trees, which are feature trees where features can have multiple occurrences. It is shown how an important class of feature models can be transformed into generalised feature trees. We present algorithms which, after transforming a feature model to a generalised feature tree, compute properties of the corresponding software product line. We discuss the computational complexity of these algorithms and provide executable specifications in the functional programming language Miranda

Poisson random fields for dynamic feature models

We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features. This model is suitable as a prior in Bayesian nonparametric feature allocation models in which the features underlying the observed data exhibit a dependency structure over time. More specifically, we establish a new framework for generating dependent Indian buffet processes, where the Poisson random field model from population genetics is used as a way of constructing dependent beta processes. Inference in the model is complex, and we describe a sophisticated Markov Chain Monte Carlo algorithm for exact posterior simulation. We apply our construction to develop a nonparametric focused topic model for collections of time-stamped text documents and test it on the full corpus of NIPS papers published from 1987 to 2015

Analysis of Feature Models Using Alloy: A Survey

Feature Models (FMs) are a mechanism to model variability among a family of closely related software products, i.e. a software product line (SPL). Analysis of FMs using formal methods can reveal defects in the specification such as inconsistencies that cause the product line to have no valid products. A popular framework used in research for FM analysis is Alloy, a light-weight formal modeling notation equipped with an efficient model finder. Several works in the literature have proposed different strategies to encode and analyze FMs using Alloy. However, there is little discussion on the relative merits of each proposal, making it difficult to select the most suitable encoding for a specific analysis need. In this paper, we describe and compare those strategies according to various criteria such as the expressivity of the FM notation or the efficiency of the analysis. This survey is the first comparative study of research targeted towards using Alloy for FM analysis. This review aims to identify all the best practices on the use of Alloy, as a part of a framework for the automated extraction and analysis of rich FMs from natural language requirement specifications.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation

Feature models are used to specify variability of user-configurable systems as appearing, e.g., in software product lines. Software product lines are supposed to be long-living and, therefore, have to continuously evolve over time to meet ever-changing requirements. Evolution imposes changes to feature models in terms of edit operations. Ensuring consistency of concurrent edits requires appropriate conflict detection techniques. However, recent approaches fail to handle crucial subtleties of extended feature models, namely constraints mixing feature-tree patterns with first-order logic formulas over non-Boolean feature attributes with potentially infinite value domains. In this paper, we propose a novel conflict detection approach based on symbolic graph transformation to facilitate concurrent edits on extended feature models. We describe extended feature models formally with symbolic graphs and edit operations with symbolic graph transformation rules combining graph patterns with first-order logic formulas. The approach is implemented by combining eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Configurable Feature Models

Feature models represent all the products that can be built under a variability-intensive system such as a software product line, but they are not fully configurable. There exist no explicit effort in defining configuration models that enable making decisions on attributes and cardinalities in feature models that use these artefacts. In this paper we present configurable feature models as an evolution from feature models that integrate configuration models within, improving the configurability of variability-intensive systems