Is Computer Science Science?

Is Computer Science Science?

5 Pages · 2012 · 89 KB · English

been part of the mainstream per- ception of computer science. But soon it will be. References. 1. Abelson, H.G. and Sussman, G.J. Structure and Interpretation of Computer Programs, 2nd ed. MIT Press, 1996. 2. Denning, P. Great principles of computing. Commun. ACM 46, 10 (Nov. 2003), 15–20. 3.

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COMMUNICATIONS OF THE ACMApril 2005/Vol 48, No 427 What is your profession? Computer science Oh? Is that a science? Sure, it is the science of infor mation processes and their inter actions with the world I’ll accept that what you do is tech nology; but not science Science deals with fundamental laws of nature Computers are man made Their principles come from other fields such as physics and electronics engineering Hold on There are many natural information processes Computers are tools to imple ment, study, and predict them In the US alone, nearly 200 academic departments recognize this; some have been granting CS degrees for 40 years They all partake of a mass delu sion The pioneers of your field gen uinely believed in the 1950s that their new field was science They were mistaken There is no computer science Computer art, yes Com puter technology, yes But no science The modern term, Information Technology, is closer to the truth I don’t accept your statements about my field and my degree Do you mind if we take a closer look? Let’s examine the accepted criteria for science and see how computing stacks up I’m listening Common Understandings of Science O ur field was called com puter science from its beginnings in the 1950s Over the next four decades, we accumulated a set of principles that extended beyond its original mathematical foundations to include computational science, systems, engineering, and design The 1989 report, Computing as a Discipline , defined the field as: “The discipline of computing is the systematic study of algo rithmic processes that describe and transform information: their theory, analysis,design, efficiency, imple mentation, and application The fundamental question underlying all ofcomputing is, ‘What canbe (efficiently) auto mated?’” [3, p 12] Science, engineering, and mathematics com bine into a unique and potent blend in our field Some of our activities are primarily science—for example, experimental algorithms, experi mental computer science, and computational science Some are primarily engineering—for exam ple, design, development, soft ware engineering, and computer engineering Some are primarily mathematics—for example, com putational complexity, mathe matical software, and numerical analysis But most are combina tions All three sets of activities JASON SCHNEIDER The Profession of IT Peter J Denning Is Computer Science Science? Computer science meets every criterion for being a science, but it has a selfinflicted credibility problem 28April 2005/Vol 48, No 4 COMMUNICATIONS OF THE ACM draw on the same fundamental principles In 1989, we used the term “computing” instead of “computer science, mathematics, and engineering” Today, com puting science, engineering, mathematics, art, and all their combinations are grouped under the heading “computer science” The scientific paradigm, which dates back to Francis Bacon, is the process of forming hypothe ses and testing them through experiments; successful hypotheses become models that explain and predict phenomena in the world Computing science follows this paradigm in studying information processes The European synonym for computer science—informat ics—more clearly suggests the field is about information processes, not computers The lexicographers offer two additional distinctions One is between pure and applied science; pure science focuses on knowl edge for its own sake and applied focuses on knowledge of demon strable utility The other is between inexact (qualitative) and exact (quantitative) science; exact science deals with prediction and verification by observation, mea surement, and experiment Computing research is rife with examples of the scientific paradigm Cognition researchers, for example, hypothesize that much intelligent behavior is the result of information processes in brains and ner vous systems; they build systems that implement hypothesized information processes and compare them with the real thing The com puters in these studies are tools to test the hypothesis; successful sys tems can be deployed immedi ately Software engineering researchers hypothesize models for how programming is done and how defects arise; through testing they seek to understand which models work well and how to use them to create better pro grams with fewer defects Experi mental algorithmicists study the performance of real algorithms on real data

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