MUSA: A Scalable Multi-Touch and Multi-Perspective Variability Management Tool

Variability management is one of the main activities in the Software Product Line Engineering process. Common and varied features of related products are modelled along with the dependencies and relationships among them. With the increase in size and complexity of product lines and the more holistic systems approach to the design process, managing the ever- growing variability models has become a challenge. In this paper, we present MUSA, a tool for managing variability and features in large-scale models. MUSA adopts the Separation of Concerns design principle by providing multiple perspectives to the model, each conveying different set of information. The demonstration is conducted using a real-life model (comprising of 1000+ features) particularly showing the Structural View, which is displayed using a mind-mapping visualisation technique (hyperbolic trees), and the Dependency View, which is displayed graphically using logic gates.

A Scalable Design Framework for Variability Management in Large-Scale Software Product Lines

Variability management is one of the major challenges in software product line adoption, since it needs to be efficiently managed at various levels of the software product line development process (e.g., requirement analysis, design, implementation, etc.). One of the main challenges within variability management is the handling and effective visualization of large-scale (industry-size) models, which in many projects, can reach the order of thousands, along with the dependency relationships that exist among them. These have raised many concerns regarding the scalability of current variability management tools and techniques and their lack of industrial adoption. To address the scalability issues, this work employed a combination of quantitative and qualitative research methods to identify the reasons behind the limited scalability of existing variability management tools and techniques. In addition to producing a comprehensive catalogue of existing tools, the outcome form this stage helped understand the major limitations of existing tools. Based on the findings, a novel approach was created for managing variability that employed two main principles for supporting scalability. First, the separation-of-concerns principle was employed by creating multiple views of variability models to alleviate information overload. Second, hyperbolic trees were used to visualise models (compared to Euclidian space trees traditionally used). The result was an approach that can represent models encompassing hundreds of variability points and complex relationships. These concepts were demonstrated by implementing them in an existing variability management tool and using it to model a real-life product line with over a thousand variability points. Finally, in order to assess the work, an evaluation framework was designed based on various established usability assessment best practices and standards. The framework was then used with several case studies to benchmark the performance of this work against other existing tools.