A major, but too-little-considered problem for Software Engineering is a lack of consensus concerning Computer Science and how this relates to developing unpredictable computing technology. We consider some implications for SE of computer systems differing scientific basis, exemplified with the International Standard Organisations Open Systems Interconnection layered architectural model. An architectural view allows comparison of computing technology components facilitating a view of computing as a continuum. For example, at one layer of computer architecture, components written in Turing-complete machine language can (...) be seen as deterministic and consistent with a theoretical paradigm of CS. At another layer, components closer to the human sphere have been seen as non-deterministic and inconsistent with theoretical CS. We compare unpredictable development of computing technology against the cyclic legacy of technological advance and scientific discovery, and suggest that SE indicates an enabling cycle, discernible in previous scientific revolution, is stalled or possibly hidden. The CS consequence of divorcing technological advance from scientific consensus is particularly concerning. For example human/computing events could be seen as unpredictable virtual phenomena that somehow extend the ontology of CS. Our approach challenges practical and philosophical boundaries by investigating if applying scientific method resolves any SE/Science dichotomy. (shrink)

Experiments in computing share many characteristics with the traditional experimental method, but also present significant differences from a practical perspective, due to their aim at producing software artifacts and the central role played by human actors and organizations involved in the software development process. By analyzing some of the most significant experiments in the subfield of software engineering, we aim at showing how the conceptual framework that supports experimental methodology in this context needs an extension in a socio-technical perspective.

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, (...) they possess peculiar and unique features. We argue that this experimental perspective should be taken into account when discussing the status of computing. We critically survey how the experimental method has been conceived and applied in computing, and some open issues that could be tackled with the aid of the history of science, the philosophy of science, and the philosophy of technology. (shrink)

This article presents the evolution of philosophical and methodological considerations concerning empiricism in computer/computing science. In this study, we trace the most important current events in the history of reflection on computing. The forerunners of Artificial Intelligence H.A. Simon and A. Newell in their paper Computer Science As Empirical Inquiry started these considerations. Later the concept of empirical computer science was developed by S.S. Shapiro, P. Wegner, A.H. Eden and P.J. Denning. They showed various empirical aspects of computing. This led (...) to a view of the science of computing - the science of general scope. Some interesting contemporary ways towards a generalized perspective on computations were also shown. (shrink)

Recently, information retrieval is shown to be a science by mapping information retrieval scientific study to scientific study abstracted from physics. The exercise was rather tedious and lengthy. Instead of dealing with the nitty gritty, this paper looks at the insights into how computer science can be made into a science by using that methodology. That is by mapping computer science scientific study to the scientific study abstracted from physics. To show the mapping between computer science and physics, we need (...) to define what is engineering science which computer science belongs to. Some principles and assumptions of engineering science theory are presented. To show computer science is a science, we presented two approaches. Approach 1 considers computer science as simulation of human behaviour similar to the goal of artificial intelligence. Approach 2 is closely related to the actual activities in computer science, and this approach considers computer science based on the theory of computation. Finally, we answer some of the common outstanding issues about computer science to convince our reader that computer science is a science. (shrink)

On the basis of an earlier contribution to the philosophy of computer science by Amnon Eden, this essay discusses to what extent Eden’s ‘paradigms’ of computer science can be transferred or applied to software engineering. This discussion implies an analysis of how software engineering and computer science are related to each other. The essay concludes that software engineering can neither be fully subsumed by computer science, nor vice versa. Consequently, also the philosophies of computer science and software engineering—though related to (...) each other—are not identical branches of a general philosophy of science. This also implies that not all of Eden’s earlier arguments can be directly mapped from the domain of computer science into the domain of software science. After the discussion of this main topic, the essay also points to some further problems and open issues for future studies in the philosophy of software science and engineering. (shrink)

The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.

Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...) are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology—what I call recasting—and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation. (shrink)

I critically review Raymond Turner’s Computational Artifacts – Towards a Philosophy of Computer Science by placing beside his position a rather different one, according to which computer science is a branch of, and is therefore subsumed by, immaterial formal logic.

This paper offers a review of Giuseppe Primero’s (2020) book “On the foundations of computing”_._ Mathematical, engineering, and experimental foundations of the science of computing are examined under the light of the notions of formal, physical, and experimental computational validity provided by the author. It is challenged the thesis that experimental computational validity can be defined only for the algorithmic method and not for the software development process. The notions of computational hypothesis and computational experiment provided by Primiero (2020) are (...) extended to the case of software development. Finally, it is highlighted how the hypothetical-deductive method is involved in the practice of using models to corroborate computational hypotheses in software testing. As a concluding remark, it is underlined how defining experimental computational validity in the context of software development offers a sound experimental foundation to the science of computing. (shrink)

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 (...) abstractions that include only data which are sufficient to examine the interested executions. The present study highlights how the need to maximize incompatible properties is at the basis of both Abstraction Refinement, the process of generating a cascade of computational models to achieve a balance between simplicity and informativeness, and the Multiple Model Idealization approach in biology. Finally, fairness constraints, imposed to computational models to allow fair behaviours only, are defined as ceteris paribus conditions under which temporal formulas, formalizing software requirements, acquire the status of law-like statements about the software systems executions. (shrink)

This article is an overview of the philosophy of informatics with a special regard to some Polish philosophers. It juxtaposes the informationistic worldview with the long-prevailing mechanical conceptualization of nature before introducing the metaphysical perspective of the information revolution in sciences. The article shows also how ontic pancomputationalism – regarded as an update to structural realism – could enrich the philosophical research in some classical topics. The paper concludes with a discussion of the philosophy of Jan Salamucha, a philosopher from (...) the Cracow Circle whose ideas could be inspiring for today’s philosophy of informatics in Cracow. (shrink)

Una imagen muy generalizada a la hora de entender el software de computador es la que lo representa como una “caja negra”: no importa realmente saber qué partes lo componen internamente, sino qué resultados se obtienen de él según ciertos valores de entrada. Al hacer esto, muchos problemas filosóficos son ocultados, negados o simplemente mal entendidos. Este artículo discute tres unidades de análisis del software de computador, esto es, las especificaciones, los algoritmos y los procesos computacionales. El objetivo central es (...) entender las prácticas cientficas e ingenieriles detrás de cada unidad de software, así como analizar su metodología, ontología y epistemología. (shrink)