CASE Tool Support for Variability Management in Software Product Lines

Abstract

Software product lines (SPL) aim at reducing time-to-market and increasing software quality through extensive, planned reuse of artifacts. An essential activity in SPL is variability management, i.e., defining and managing commonality and variability among member products. Due to the large scale and complexity of today's software-intensive systems, variability management has become increasingly complex to conduct. Accordingly, tool support for variability management has been gathering increasing momentum over the last few years and can be considered a key success factor for developing and maintaining SPLs.While several studies have already been conducted on variability management, none of these analyzed the available tool support in detail. In this work, we report on a survey in which we analyzed 37 existing variability management tools identified using a systematic literature review to understand the tools' characteristics, maturity, and the challenges in the field. We conclude that while most studies on variability management tools provide a good motivation and description of the research context and challenges, they often lack empirical data to support their claims and findings. It was also found that quality attributes important for the practical use of tools such as usability, integration, scalability, and performance were out of scope for most studies. CCS Concepts: • General and reference → Surveys and overviews • Software and its engineering → Software product lines • Software and its engineering → Software notations and tools • Software and its engineering → Software configuration management and version control systems XX:2 • R. Bashroush et al.Defining and managing commonalities and variability in software product lines is widely referred to as variability management and is a key step of the SPL engineering process [van Gurp et al. 2001]. The variability management process guides the construction of product line variability models. Different types of variability models have been proposed, e.g., feature models, decision models, Orthogonal Variability Models (OVM), and UML-based approaches. In Section 1.1 we provide an overview of existing modeling approaches. For a detailed comparison and classification of variability modeling approaches we refer to [Czarnecki et al. 2012] and [Sinnema and Deelstra 2007]. Variability models define the commonalities and variability of the product line from a problem space (e.g., features, decisions, or variation points) and a solution space (e.g., the reusable assets or variants) perspective along with the relationships that exist between these two spaces and among the elements in these spaces. Example relationships include exclusivity (when two features cannot exist in one product at the same time); inclusivity (when the existence of one feature depends on another); and alternatives (when only one of a number of alternative features can be supported), to name a few. Variability models tend to be very large in size, in many cases comprising thousands of features, and comp...

A systematic literature review on the semi-automatic configuration of extended product lines

“…For instance, Bashroush et al (2017); Rabiser et al (2010) identified a set of requirements for tool-support during product derivation (e.g. automated and interactive variability resolution, variability visualisation); da Silva et al (2013) reviewed articles related to dynamic SPLs.…”

“…Although there are available tools (Pereira et al, 2014;Bashroush et al, 2017) to partially assist decision makers during the semi-automatic configuration process of extended PLs (e.g. VariaMos , jUCMNav , Kumbang , MO-DAGAME , SALOON , PuMA , SPL Conqueror , and Ascent Desigh Studio and FaMa ), those tools do not offer support to the whole set of the SLR identified configuration constraints and there is also a lack of visualisation mechanisms.…”

Model-based systems engineering with requirements variability for embedded real-time systems

“… "Binding time" is a tag that indicates when a certain variation point should be instantiated. It is important to specify when to bind a specific variant to its variation point [47]. A typical binding time might be "Domain Requirement Stage", which means "determine the variation point when considering requirement commonality and variability", or "Application Requirement Stage", which means "decisions on the variation points are made when specific customers' requirements arrive".…”

Principles of feature modeling

“…2). Feature modeling is supported by major product-line engineering tools [5], such as pure::variants [15], Gears [53], and FeatureIDE [77]. Many researchers, practitioners, and tool vendors have introduced additional concepts, such as feature attributes, feature cardinalities [6,7,25] or non-propositional constraints [14,65], thus gradually increasing the expressiveness of feature models.…”