Where Is the Science in Computer Science?. Cerf, V. G. 55(10):5. ![Where Is the Science in Computer Science? [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex [Excerpt] [...] The term "computer science" raises expectations, at least to my mind, of an ability to define models and to make predictions about the behavior of computers and computing systems. I think we have a fairly good capability to measure and predict the physical performance of our computing devices. We can measure clock speeds, latencies, memory sizes, and computational capacity against standard computing tasks. In my view, however, we are much less able to make models and predictions about the behavior and performance of the artifact we label "software." An almost flippant analogy is the difference between measuring, modeling, and predicting neural brain functions and trying to do the same for "thought." [...] [] When we write a piece of software, do we have the ability to predict how many mistakes we have made (that is, bugs)? Do we know how long it will take to find and fix them? Do we know how many new bugs our fixes will create? Can we say anything concrete about vulnerability? What about the probability of exploitation? Murphy's Law suggests that if there is a bug that can be exploited for nefarious purposes, it will be. [...] [] [...] we try to bound complexity through modular and hierarchical structures but are still challenged by the emergent behaviors masked, in some ways, by the beguiling apparent simplicity of the hierarchy. The richness of our field has only grown in the 65 years of our existence as an organization. Computers, computing, software, and systems are seemingly omnipresent. We are growing increasingly dependent upon what must be billions of lines of code. [...]
@article{cerfWhereScienceComputer2012,
title = {Where Is the Science in Computer Science?},
author = {Cerf, Vinton G.},
date = {2012-10},
journaltitle = {Commun. ACM},
volume = {55},
pages = {5},
issn = {0001-0782},
doi = {10.1145/2347736.2347737},
url = {https://doi.org/10.1145/2347736.2347737},
abstract = {[Excerpt] [...] The term "computer science" raises expectations, at least to my mind, of an ability to define models and to make predictions about the behavior of computers and computing systems. I think we have a fairly good capability to measure and predict the physical performance of our computing devices. We can measure clock speeds, latencies, memory sizes, and computational capacity against standard computing tasks. In my view, however, we are much less able to make models and predictions about the behavior and performance of the artifact we label "software." An almost flippant analogy is the difference between measuring, modeling, and predicting neural brain functions and trying to do the same for "thought." [...]
[] When we write a piece of software, do we have the ability to predict how many mistakes we have made (that is, bugs)? Do we know how long it will take to find and fix them? Do we know how many new bugs our fixes will create? Can we say anything concrete about vulnerability? What about the probability of exploitation? Murphy's Law suggests that if there is a bug that can be exploited for nefarious purposes, it will be. [...]
[] [...] we try to bound complexity through modular and hierarchical structures but are still challenged by the emergent behaviors masked, in some ways, by the beguiling apparent simplicity of the hierarchy. The richness of our field has only grown in the 65 years of our existence as an organization. Computers, computing, software, and systems are seemingly omnipresent. We are growing increasingly dependent upon what must be billions of lines of code. [...]},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-11310407,computational-science,cross-disciplinary-perspective,modelling,software-errors,software-uncertainty},
number = {10}
}
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