Metaphor in computer science

AbstractThe language of computer science is laced with metaphor. We argue that computer science metaphors provide a conceptual framework in which to situate constantly emerging new ontologies in computational environments. But how computer science metaphors work does not fit neatly into prevailing general theories of metaphor. We borrow from these general theories while also considering the unique role of computer science metaphors in learning, design, and scientific analysis. We find that computer science metaphors trade on both preexisting and emerging similarities between computational and traditional domains, but owing to computer science's peculiar status as a discipline that creates its own subject matter, the role of similarity in metaphorical attribution is multifaceted

Philosophical conceptions of information

“The original publication is available at www.springerlink.com” Copyright Springer'I love information upon all subjects that come in my way, and especially upon those that are most important.' Thus boldly declares Euphranor, one of the defenders of Christian faith in Berkley’s Alciphron (Berkeley, (1732), Dialogue 1, Section 5, Paragraph 6/10). Evidently, information has been an object of philosophical desire for some time, well before the computer revolution, Internet or the dot.com pandemonium (see for example Dunn (2001) and Adams (2003)). Yet what does Euphranor love, exactly? What is information? The question has received many answers in different fields. Unsurprisingly, several surveys do not even converge on a single, unified definition of information (see for example Braman 1989, Losee (1997), Machlup and Mansfield (1983), Debons and Cameron (1975), Larson and Debons (1983)).Peer reviewe

Software as Learning: Quality Factors and Life-Cycle Revised

. In this paper Software Development (SD) is understood explicitly as a learning process, which relies much more on induction than deduction, with the main goal of being predictive to requirements evolution. Concretely, classical processes from philosophy of science and machine learning such as hypothesis generation, refinement, confirmation and revision have their counterpart in requirement engineering, program construction, validation and modification in SD, respectively. Consequently, we have investigated the appropriateness for software modelling of the most important paradigms of modelling selection in machine learning. Under the notion of incremental learning, we introduce a new factor, predictiveness, as the ability to foresee future changes in the specification, thereby reducing the number of revisions. As a result, other quality factors are revised. Finally, a predictive software life cycle is outlined as an incremental learning session, which may or may not be aut..