2011/12/01 by Rafael Capilla, Jan Bosch
Computer Science · Business, Management and Accounting · #Advanced Software Engineering Methodologies #Service-Oriented Architecture and Web Services #Business Process Modeling and Analysis
paper · doi:10.1109/mc.2011.382
Runtime variability offers a good choice for many systems that experience dynamic changes in their quality and context. Since the advent of software product lines (SPLs) in the 1990s as a successful approach for building multiple, related products, feature models (FMs)-compact representations of all the features of the products in the SPL-have increased in popularity. Large and medium- size software companies now rely on numerous second-generation SPL tools such as Gears SPL Lifecycle Framework (www.biglever.com) and pure::variants (www.pure-systems, com) that use FMs to describe the variability of their products. Today, systems with adaptive and context-aware architectures- including autonomic and ubiquitous computing systems and software ecosystems-require more dynamic capabilities to address runtime needs (S. Hallsteinsen et al., "Dynamic Software Product Lines," Computer, Apr. 2008, pp. 93-95). In service-oriented computing, for example, the selection of services motivated by changing conditions or service-level agreements implies an automated binding of the current service to a different one. Likewise, autonomous software designed to load a new system configuration, based on a set of allowed choices, must cope with different situations. Such systems must be able to adapt to runtime conditions and manage them with minimal human intervention.