A Taxonomy of Errors for Information Systems

Primiero, Giuseppe

doi:10.1007/s11023-013-9307-5

Cited by 15 publications

(2 citation statements)

References 23 publications

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“…In [Araújo(2014)], we showed some consequences for artificial intelligence of the quantitative approach to semantic information developed in this paper, but there is other important directions to be explored. For applications, we can, for instance, to define databases in terms of typed constructive structures, and so to analyze the semantic information processing in some functional programming language, as it was made in [Primiero(2013)] with respect to data errors. Moreover, with respect to the foundations of the strong theory of semantic information, it would be desirable, for example, to implement levels of abstraction into databases.…”

Section: Discussionmentioning

confidence: 99%

A quantitative approach to semantic informativity

de Araújo

2014

Preprint

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Section: Discussionmentioning

confidence: 99%

A quantitative approach to semantic informativity

de Araújo

2014

Preprint

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“…Beta is paraphrasing categorization of miscomputation by Primiero and Fresco[19,42] 14-5Learning to live with errors…”

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Miscomputation in software: Learning to live with errors

Petříček

2017

Programming

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Computer programs do not always work as expected. In fact, ominous warnings about the desperate state of the software industry continue to be released with almost ritualistic regularity. In this paper, we look at the 60 years history of programming and at the different practical methods that software community developed to live with programming errors.We do so by observing a class of students discussing different approaches to programming errors. While learning about the different methods for dealing with errors, we uncover basic assumptions that proponents of different paradigms follow. We learn about the mathematical attempt to eliminate errors through formal methods, scientific method based on testing, a way of building reliable systems through engineering methods, as well as an artistic approach to live coding that accepts errors as a creative inspiration.This way, we can explore the differences and similarities among the different paradigms. By inviting proponents of different methods into a single discussion, we hope to open potential for new thinking about errors. When should we use which of the approaches? And what can software development learn from mathematics, science, engineering and art?When programming or studying programming, we are often enclosed in small communities and we take our basic assumptions for granted. Through the discussion in this paper, we attempt to map the large and rich space of programming ideas and provide reference points for exploring, perhaps foreign, ideas that can challenge some of our assumptions. ACM CCS 2012General and reference → Surveys and overviews;Social and professional topics → History of computing; The history and classification of errorsIf trials of three or four simple cases have been made, and are found to agree with the results given by the engine, it is scarcely possible that there can be any error among the cards [5].The opening quote from Charles Babbage about the Analytical Engine suggests that Babbage did not see errors as a big problem. If that was the case, the software industry would save billions of dollars, but sadly, eradicating all software errors turned out to be harder than expected.In retrospect, it is curious to see how long it took early computer personnel to realise that coding errors are a problem. One of the first people to realize that " [he] was going to spend a good deal of [his] time finding mistakes that [he] had made in [his] programs" was Maurice Wilkes in late 1940s [17]. However, as Mark Priestley writes, a typical comment [circa 1949] was that of Miller, who wrote that such errors, along with hardware faults, could be "expected, in time, to become infrequent" [41]. While hardware faults did become relatively infrequent, the same cannot be said about coding errors.Over the next 20 years, computers developed from a research experiment into an ordinary scientific tool and industrial instrument. As noted by Nathan Ensmenger, by the end of 1960s many were talking of a computing crisis. For the next "several decades, managers, academic...

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Handling Mobility Failures by Modal Types

Primiero

2024

Logic, Epistemology, and the Unity of Science

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A Taxonomy of Errors for Information Systems

Cited by 15 publications

References 23 publications

A quantitative approach to semantic informativity

A quantitative approach to semantic informativity

Miscomputation in software: Learning to live with errors

Handling Mobility Failures by Modal Types

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