Automated Analysis Of Feature Models Research Articles

Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration

Feature models (FMs) appeared more than 30 years ago, and they are valuable tools for modeling the functional variability of systems. The automated analysis of feature models (AAFM) is currently a thriving, motivating, and active research area. The product configuration of FMs is a relevant and helpful operation, a crucial activity overall with large-scale feature models. The minimal conflict detection, the diagnosis of in-conflict configuration, and the product completion of consistent partial configuration are significant operations for obtaining consistent and well-defined products. Overall, configuring products for large-scale variability intensive systems (VIS) asks for efficient automated solutions for minimal conflict, diagnosis, and product configuration. Given the relevance of minimal conflict, diagnosis, and product configuration, and the current application of large-scale configuration and FMs for representing those systems and products, the main goals of this research paper are to establish the fundaments of the product configuration of feature models and systematically review existing solutions for the conflict detection, diagnosis, and product completion in FMs from 2010 to 2019. We can perceive that even though modern computing approaches exist for AAFM operations, no solutions exist for assisting the product configurations before 2020. This article reports that in 2020, new solutions appear regarding applying parallel computing for those goals. This research highlights research opportunities for developing new and more efficient solutions for conflict detection, diagnosis, and product completion of large-scale configurations.

Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

A Feature Model (FM) is an information model to represent commonalities and variabilities for all the products of a Software Product Line (SPL). The complexity and large-scale of real feature models makes their manual analysis for determining the product configurations validity a tedious or even infeasible task. Efficient solutions for the diagnosis of errors in the Automated Analysis of Feature Models (AAFM) already exist such as FMDiag and FlexDiag. Thus, this work describes the fundamental basis for both diagnosis algorithms to apply the first of them on the validity of FM product configurations. The results highlight the applicability and efficiency of FMDiag and invite us to look for additional applications in the AAFM scenarios.

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90s 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.