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Model-driven strategies have been used in the development of software product lines (SPLs) to facilitate product customization and to generate the source code of the derived products through variability management. In this context, the architecture description of the SPL is essential to make it clear how the architecture realizes the feature model and to represent both domain and application engineering architectural artifacts. Moreover, it is important to establish the association between the architectural specification and the artifacts involved in the SPL development process towards code generation. In this paper, we present Arch SPL-MDD, a model-driven strategy to support explicit modeling and automatic management of variabilities in SPLs. Arch SPL-MDD is associated to a generic process with systematic activities aimed to generate customized source code from the product configuration. Furthermore, the proposed strategy uses the Light PL-ACME architecture description language to support the architectural specification of the SPL, which is input of model transformations towards automatically generating SPL products. To evaluate the efficiency and applicability of Arch SPL-MDD, we conducted a controlled experiment by using Ginga For All, an SPL for the Ginga digital TV middleware.

A concern is any consideration that can impact the software architecture and other artefacts. Sustaining stability of concerns realised in architectural designs is essential to obtain long-lasting software products. It is often assumed that concern assessment mechanisms are effective to predict architecture stability. They ought to exert a positive or detrimental effect on software stability. In fact, there is a growing variety of concern assessment mechanisms to support the quantification of concern properties in software artefacts. They range from metrics that compute basic concern characteristics – such as concern tangling and scattering – to patterns that represent recurring concern structures. However, the vast majority of concern assessment mechanisms has not been empirically validated yet. This paper presents a comparative study on the efficacy of a family of concern metrics and patterns to support architectural stability analysis. The correlation of concern properties and architecture instabilities is determined by quantifying the Spearman Correlation indicator. This study relies on several releases of two evolving software systems, where the architectural instabilities have been previously and independently documented.

In this paper we propose an integrated approach for managing variabilities in architectural specifications of software product lines. Our approach combines an aspect-oriented architectural description language for product lines, PL-AspectualACME, with a variability modeling language, VML4Arch. PL-AspectualACME, also proposed in this paper, is used to specify the overall architectural description consisting of the modular representation of architecture commonalities and variabilities. VML4Arch is used to automatically derive product-specific architecture descriptions by specifying features dependencies and the variabilities that must be included in the products. We illustrate the application of our approach by using a software product line from the mobile domain.

Sustaining architecture stability in incremental software development is an important aim for software engineers. Traceability mechanisms can be used to assess and predict architecture stability based on recorded information of early software artefacts. However, there is little empirical knowledge on whether traceability of stakeholders' concerns can assist the identification of architecture instabilities. This paper reports on a first exploratory study that analyses the effectiveness of concern traces for architecture stability assessment. We investigate to what extent properties of concern traces, such as their shapes, are correlated with architectural instabilities. Our analysis is based on eight releases implementing two versions of a software product line for handling mobile media.