Kaiserslautern - Fachbereich Informatik
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The rapid development of any field of knowledge brings with it unavoidable fragmentation and proliferation of new disciplines. The development of computer science is no exception. Software engineering (SE) and human-computer interaction (HCI) are both relatively new disciplines of computer science. Furthermore, as both names suggest, they each have strong connections with other subjects. SE is concerned with methods and tools for general software development based on engineering principles. This discipline has its roots not only in computer science but also in a number of traditional engineering disciplines. HCI is concerned with methods and tools for the development of human-computer interfaces, assessing the usability of computer systems and with broader issues about how people interact with computers. It is based on theories about how humans process information and interact with computers, other objects and other people in the organizational and social contexts in
which computers are used. HCI draws on knowledge and skills from psychology, anthropology and sociology in addition to computer science. Both disciplines need ways of measuring how well their products and development processes fulfil their intended requirements. Traditionally SE has been concerned with 'how software is constructed' and HCI with 'how people use software'. Given the
different histories of the disciplines and their different objectives, it is not surprising that they take different approaches to measurement. Thus, each has its own distinct 'measurement culture.' In this paper we analyse the differences and the commonalties of the two cultures by examining the measurement approaches used by each. We then argue the need for a common measurement taxonomy and framework, which is derived from our analyses of the two disciplines. Next we demonstrate the usefulness of the taxonomy and framework via specific example studies drawn from our own work and that of others and show that, in fact, the two disciplines have many important similarities as well as differences and that there is some evidence to suggest that they are growing closer. Finally, we discuss the role of the taxonomy as a framework to support: reuse, planning future studies, guiding practice and facilitating communication between the two disciplines.
Intellectual control over software development projects requires the existence of an integrated set of explicit models of the products to be developed, the processes used to develop them, the resources needed, and the productivity and quality aspects involved. In recent years the development of languages, methods and tools for modeling software processes, analyzing and enacting them has become a major emphasis of software engineering research. The majority of current process research concentrates on prescriptive modeling of small, completely formalizable processes and their execution entirely on computers. This research direction has produced process modeling languages suitable for machine rather than human consumption. The MVP project, launched at the University of Maryland and continued at Universität Kaiserslautern, emphasizes building descriptive models of large, real-world processes and their use by humans and computers for the purpose of understanding, analyzing, guiding and improving software development projects. The language MVP-L has been developed with these purposes in mind. In this paper, we
motivate the need for MVP-L, introduce the prototype language, and demonstrate its uses. We assume that further improvements to our language will be triggered by lessons learned from applications and experiments.