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A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

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Arcuri, Andrea, Briand, Lionel·2014·Software Testing, Verification and Reliability
DOI 10.1002/stvr.1486
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Abstract

Randomized algorithms are widely used to address many types of software engineering problems, especially in the area of software verification and validation with a strong emphasis on test automation. However, randomized algorithms are affected by chance and so require the use of appropriate statistical tests to be properly analysed in a sound manner. This paper features a systematic review regarding recent publications in 2009 and 2010 showing that, overall, empirical analyses involving randomized algorithms in software engineering tend to not properly account for the random nature of these algorithms. Many of the novel techniques presented clearly appear promising, but the lack of soundness in their empirical evaluations casts unfortunate doubts on their actual usefulness. In software engineering, although there are guidelines on how to carry out empirical analyses involving human subjects, those guidelines are not directly and fully applicable to randomized algorithms. Furthermore, many of the textbooks on statistical analysis are written from the viewpoints of social and natural sciences, which present different challenges from randomized algorithms. To address the questionable overall quality of the empirical analyses reported in the systematic review, this paper provides guidelines on how to carry out and properly analyse randomized algorithms applied to solve software engineering tasks, with a particular focus on software testing, which is by far the most frequent application area of randomized algorithms within software engineering. Copyright © 2012 John Wiley & Sons, Ltd.

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Metadata

Authors
Arcuri, A., & Briand, L.
Venue
Software Testing, Verification and Reliability
Publisher
Wiley
Published
2012-11-06
Volume
24
Issue
3
Pages
219-250
ISSN
0960-0833 (ISSN-L)
DOI
10.1002/stvr.1486
OpenAlex
W1526710119
Cited by
622
References
10
FWCI
58.87
Type
journal article
Language
en
Open access
green
Licence
doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 1029 d)

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Arcuri, A., & Briand, L. (2014). A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability. https://doi.org/10.1002/stvr.1486

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