Reference : A Hitchhiker's guide to statistical tests for assessing randomized algorithms in soft...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/1071
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering
English
Arcuri, Andrea mailto [Simula Research Laboratory, Norway]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
2012
Software Testing : Verification & Reliability
John Wiley & Sons, Inc. - Engineering
Yes (verified by ORBilu)
International
0960-0833
[en] statistical difference ; effect size ; parametric test ; nonparametric test ; confidence interval ; Bonferroni adjustment ; systematic review ; survey
[en] 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.
http://hdl.handle.net/10993/1071
10.1002/stvr.1486

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