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Methodology of Computer Science

In Luciano Floridi (ed.), The Blackwell Guide to the Philosophy of Computing and Information. Oxford, UK: Blackwell. pp. 318–326 (2004)

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  1. Computing and Experiments: A Methodological View on the Debate on the Scientific Nature of Computing.Viola Schiaffonati & Mario Verdicchio - 2014 - Philosophy and Technology 27 (3):359-376.
    The question about the scientific nature of computing has been widely debated with no universal consensus reached about its disciplinary status. Positions vary from acknowledging computing as the science of computers to defining it as a synthetic engineering discipline. In this paper, we aim at discussing the nature of computing from a methodological perspective. We consider, in particular, the nature and role of experiments in this field, whether they can be considered close to the traditional experimental scientific method or, instead, (...)
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  • Decoupling as a Fundamental Value of Computer Science.Timothy Colburn & Gary Shute - 2011 - Minds and Machines 21 (2):241-259.
    Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm shifts in both natural and computer science (...)
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  • Abstraction, law, and freedom in computer science.Timothy Colburn & Gary Shute - 2010 - Metaphilosophy 41 (3):345-364.
    Abstract: Laws of computer science are prescriptive in nature but can have descriptive analogs in the physical sciences. Here, we describe a law of conservation of information in network programming, and various laws of computational motion (invariants) for programming in general, along with their pedagogical utility. Invariants specify constraints on objects in abstract computational worlds, so we describe language and data abstraction employed by software developers and compare them to Floridi's concept of levels of abstraction. We also consider Floridi's structural (...)
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  • The Theory-Practice Gap in the Evaluation of Agent-Based Social Simulations.David Anzola - 2021 - Science in Context 34 (3):393-410.
    ArgumentAgent-based social simulations have historically been evaluated using two criteria: verification and validation. This article questions the adequacy of this dual evaluation scheme. It claims that the scheme does not conform to everyday practices of evaluation, and has, over time, fostered a theory-practice gap in the assessment of social simulations. This gap originates because the dual evaluation scheme, inherited from computer science and software engineering, on one hand, overemphasizes the technical and formal aspects of the implementation process and, on the (...)
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  • Abstraction and Idealization in the Formal Verification of Software Systems.Nicola Angius - 2013 - Minds and Machines 23 (2):211-226.
    Questions concerning the epistemological status of computer science are, in this paper, answered from the point of view of the formal verification framework. State space reduction techniques adopted to simplify computational models in model checking are analysed in terms of Aristotelian abstractions and Galilean idealizations characterizing the inquiry of empirical systems. Methodological considerations drawn here are employed to argue in favour of the scientific understanding of computer science as a discipline. Specifically, reduced models gained by Dataion are acknowledged as Aristotelian (...)
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  • The philosophy of computer science.Raymond Turner - 2013 - Stanford Encyclopedia of Philosophy.