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A taxonomy of software types to facilitate search and evidence-based software engineering

Published: 27 October 2008 Publication History

Abstract

Empirical software research could be improved if there was a systematic way to identify the types of software for which empirical evidence applies. This is because results are unlikely to be globally applicable, but are more likely to apply only in certain contexts such as the type of software on which the evidence has been tested. We present a software taxonomy that should help researchers to apply their research systematically to particular types of software. The taxonomy was generated using existing partial taxonomies and input from survey participants. If a taxonomy such as ours gains acceptance, it will facilitate comparison and appropriate application of research. In the paper, we present the benefits of such a taxonomy, the process we used to develop it, and the taxonomy itself.

References

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Vessey, I. 1997. Problems versus solutions: the role of the application domain in software. Seventh Workshop on Empirical Studies of Programmers (Alexandria, Virginia, United States). S. Wiedenbeck and J. Scholtz, Eds. ESP '97. ACM, New York, NY, 233--240.
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Forward A. Study: Building a software taxonomy. Retrieved 12/14, 2007, from http://cruise.site.uottawa.ca/awf/.
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Forward A. Why build a software taxonomy? Retrieved 12/14, 2007, from http://www.youtube.com/watch?v=iqlnWIFZ 5BM.
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Forward A. Tutorial - How to create a software taxonomy. Retrieved 12/14, 2007, from http://www.youtube.com/watch?v=Ut2O8xze IMI.
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Forward A. Latest software taxonomy. Retrieved 12/14, 2007, from http://www.site.uottawa.ca/~tcl/gradtheses/aforwardphd/Software%20Taxonomy.doc
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Forward, A. Modeling survey data and software application taxonomy data available at http://site.uottawa.ca/~tcl/gradtheses/aforwardphd/.

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cover image ACM Other conferences
CASCON '08: Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
October 2008
357 pages
ISBN:9781450378826
DOI:10.1145/1463788
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 27 October 2008

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  • (2024)Demystifying API misuses in deep learning applicationsEmpirical Software Engineering10.1007/s10664-023-10413-929:2Online publication date: 16-Feb-2024
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