Rich Internet Applications refers to Web applications resembling desktop solutions with sophisticated user interactions, client-side processing, and asynchronous communications. Rich Internet applications have been evolved from traditional multi-page Web applications to single page applications which handle users' interactions without the need of full-refresh at each interaction. Although many techniques, methodologies, and technologies have been proposed in the rich Internet applications literature, the need for managing variability has not yet been conveniently addressed in this domain. According to software product lines research and practice, handling variability and commonality plays an important role in decreasing the development time and improving the quality of nal products. To this end, in this paper, we aim at employing the variability management techniques in the domain of rich Internet applications. We propose a variability modeling technique based on well-known feature modeling approach and provide a method for annotating rich Internet applications with variability and deriving final application based the given con guration. The proposed method is implemented in a tool named Varion which can be used along with existing rich Internet application tools and approaches. We applied the proposed approach on Angular, a well-known Model-View-Controller framework for developing rich Internet applications.

Software Product Line (SPL) engineering is a systematic reuse-based software development approach which is founded on the idea of building software products using a set of core assets rather than developing individual software systems from scratch. Feature models are among the widely used artefacts for SPL development that mostly capture functional and operational variability of a system. Researchers have argued that connecting intentional variability models such as goal models with feature variability models in a target domain can enrich feature models with valuable quality and non-functional information. Interrelating goal models and feature models has already been proposed in the literature for capturing non-functional properties in software product lines; however, this manual integration process is cumbersome and tedious. In this paper, we propose a (semi) automated approach that systematically integrates feature models and goal models through standard ontologies. Our proposed approach connects feature model and goal model elements through measuring the semantic similarity of their annotated ontological concepts. Our work not only provides the means to systematically interrelate feature models and goal models but also allows domain engineers to identify and model the role and significance of non-functional properties in the domain represented by the feature model.

Requirements engineering is recognized as a critical stage in software development lifecycle. Given the nature of Software Product Lines (SPL), the importance of requirements engineering is more pronounced as SPLs pose more complex challenges than development of a 'single' product. Several methods have been proposed in the literature, which encompass activities for capturing requirements, their variability and commonality. To investigate the maturity and effectiveness of the current requirements engineering approaches in software product lines, we develop an evaluation framework containing a set of evaluation criteria and assess feature oriented requirements engineering methods based on the proposed criteria. As a result of this initial study, we find out the majority of approaches lacks proper techniques for supporting the validation of family requirements models as well as dealing with delta requirements. Additionally, capturing stakeholders' preferences and applying them during the course of software feature configuration have not been taken into account and addressed in the proposed approaches.

Feature modeling is one of the main techniques used in Software Product Line Engineering to manage the variability within the products of a family. Concrete products of the family can be generated through a configuration process. The configuration process selects and/or removes features from the feature model according to the stakeholders' requirements. Selecting the right set of features for one product from amongst all of the available features in the feature model is a complex task because: 1) the multiplicity of stakeholders' functional requirements; 2) the positive or negative impact of features on non-functional properties; and 3) the stakeholders' preferences w.r.t. the desirable non-functional properties of the final product. Many configurations techniques have already been proposed to facilitate automated product derivation. However, most of the current proposals are not designed to consider stakeholders' preferences and constraints especially with regard to non-functional properties. We address the software product line configuration problem and propose a framework, which employs an artificial intelligence planning technique to automatically select suitable features that satisfy both the stakeholders' functional and non-functional preferences and constraints. We also provide tooling support to facilitate the use of our framework. Our experiments show that despite the complexity involved with the simultaneous consideration of both functional and non-functional properties our configuration technique is scalable.

In Software Product Line Engineering, concrete products of a family can be generated through a configuration process over a feature model. The configuration process selects features from the feature model according to the stakeholders' requirements. Selecting the right set of features for one product from all the available features in the feature model is a cumbersome task because 1) the stakeholders may have diverse business concerns and limited resources that they can spend on a product and 2) features may have negative and positive contributions on different business concern. Many configurations techniques have been proposed to facilitate software developers' tasks through automated product derivation. However, most of the current proposals for automatic configuration are not devised to cope with business oriented requirements and stakeholders' resource limitations. We propose a framework, which employs an artificial intelligence planning technique to automatically select suitable features that satisfy the stakeholders' business concerns and resource limitations. We also provide tooling support to facilitate the use of our framework.

Information systems are expected to satisfy increasingly ambitious requirements, while reducing time-to-market has become a primary objective. This trend has necessitated the advent of development approaches that are better equipped and flexible enough to cope with modern challenges. Model-Driven Architecture (MDA) and Situational Method Engineering (SME) are approaches addressing this requirement: MDA provides promising means for automating the software process, and revitalizes the role of modeling in software development, SME focuses on project-specific methodology construction, mainly through assembling reusable method fragments (process patterns) retrieved from a method base. We provide a set of high-level process patterns for model-driven development which have been derived from a study of six prominent MDA-based methodologies, and which form the basis for a proposed generic MDA Software Process (MDASP). These process patterns can promote SME by providing classes of common process components which can be used for assembling, tailoring, and extending MDA-based methodologies.