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.

Constructing Personal and Couple Narratives in Late Stage Cancer: A Narrative Analysis

An increasing number of people with terminal cancer are being cared for at home, often by their partner. This study explores the identity, experiences and relationships of people caring for their partner at the end of life and how they construct their experience through personal and couple narratives. It draws upon dialogical approaches to narrative analysis to focus on caring partners and the care relationship. Six participants were recruited for the study. Two methods of data collection are used: narrative interviews and journals. Following individual case analysis, two methods of cross-narrative analysis are used: an analysis of narrative themes and an identification of narrative types. The key findings can be summarised as follows. First, in the period since their partner's terminal prognosis, participants sustained and reconstructed self and couple relationship narratives. These narratives aided the construction of meaning and coherence at a time of major biographical disruption: the anticipated loss of a partner. Second, the study highlights the complexity of spoken and unspoken narratives in terminal cancer and how these relate to individual and couple identities. Third, a typology of archetypal narratives based upon the data is identified. The blow-by-blow narratives illustrate how participants sought to construct coherence and meaning in the illness story, while champion and resilience narratives demonstrate how participants utilised positive self and relational narratives to manage a time of biographical disruption. The study highlights how this narrative approach can enhance understanding of the experiences and identities of people caring for a terminally ill partner.