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Abstract. Is Open Source Software (OSS) undergoing a transformation to a more commercially viable form? We have performed a survey to investigate the adoption of OSS in the Norwegian software industry. The survey was based on an extensive screening of software companies, with more than 700 responses. The survey results support the transformation predicted by Fitzgerald [4]. Close to 50% of the software industry integrate OSS components into vertical solutions serving all major business sectors. In addition, more than 30% of the 95 respondents in our survey have more than 40% of their income from OSS related services or software. The extensive adoption of OSS in the software industry may be a precursor of the OSS adoption in other business sectors.

Growing attention on component-based development has inspired the development of several normative methods for selection of software components. Despite these efforts, empirical studies show only minor adoption of such methods. To understand how research can contribute to improving the selection of components we interviewed developers from 16 Norwegian software companies which integrate Open Source Software (OSS) components into their systems. We find that the selection of OSS components has a situational nature where project specific properties significantly constrain the selection's outcome, and that developers employ a 'first fit' rather than 'best fit' approach when selecting OSS components. This could explain the limited adoption of normative selection approaches and general evaluation schemas. Moreover, it motivates a shift from developing such methods and schemas towards understanding the situational nature of software selection.

A way to learn about Commercial Off-The-Shelf (COTS) products is to define a set of characteristics or attributes and then to collect information about these attributes. In an industrial context, the attributes used to select COTS clearly depend on project specific goals. In our educational context we made an attempt to define general COTS characterization attributes. The resulting framework provides a structure, which facilitate the learning process. Our proposed attributes have several similarities with the generic evaluation attributes defined by ISO 9126. A comparison with such a standard provides a deeper insight into the problem of characterizing COTS products.

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A way to learn about Commercial Off-The-Shelf (COTS) products is to define a set of characteristics or attributes and then to collect information about these attributes. In an industrial context, the attributes used to select COTS clearly depend on project specific goals. In our educational context we made an attempt to define general COTS characterization attributes. The resulting framework provides a structure, which facilitate the learning process. Our proposed attributes have several similarities with the generic evaluation attributes defined by ISO 9126. A comparison with such a standard provides a deeper insight into the problem of characterizing COTS products.

Abstract:The evolution of mobile and ubiquitous technologies gives promises for computational services and resources to support and influence work processes planned or performed in physical work environments. Such support should optimally provide the workers with a safer work environment for both the environment itself and the workers. The extended support can give more economic and optimal work processes through proactive and situated planning and execution. We introduce the concept of a smart work process to capture adaptive and context-aware process support. This combination of ubiquitous computing and workflow defines a new research direction to be investigated. This paper elaborates on research challenges related to how smart work processes can be supported. We present and discuss general cases where context information or change in context information influences mobile work activities. Finally, we propose a framework for modelling smart work processes, and present a high-level architecture to support smart work processes.