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Minimization of Randomized Unit Test Cases Yong Lei and James H. Andrews

Summary: Minimization of Randomized Unit Test Cases
Yong Lei and James H. Andrews
Department of Computer Science
University of Western Ontario
London, Ontario, CANADA N6A 5B7
Email: fleiyong,andrewsg (at) csd.uwo.ca
Abstract--- We describe a framework for randomized unit
testing, and give empirical evidence that generating unit test
cases randomly and then minimizing the failing test cases results
in significant benefits. Randomized generation of unit test cases
(sequences of method calls) has been shown to allow high
coverage and to be highly effective. However, failing test cases, if
found, are often very long sequences of method calls. We show
that Zeller and Hildebrandt's test case minimization algorithm
significantly reduces the length of these sequences. We study
the resulting benefits qualitatively and quantitatively, via a case
study on found open­source data structures and an experiment
on lab­built data structures.
Software testing consists of three main activities: selecting


Source: Andrews, Jamie - Department of Computer Science, University of Western Ontario


Collections: Computer Technologies and Information Sciences