Journal article
Information gain of black-box testing
Formal aspects of computing, Vol.23(4), pp.513-539
07/2011
Handle:
https://hdl.handle.net/2376/109897
Abstract
For model-based black-box testing, test cases are often selected from the syntactic appearance of the specification of the system under test, according to a pre-given test data adequacy criterion. We introduce a novel approach that is semantics-based, independent of the syntactic appearance of the system specification. Basically, we model the system under test as a random variable, whose sample space consists of all possible behavior sets (with respect to the specification) over the known interface of the black-box. The entropy of the system is measured as the (Shannon) entropy of the random variable. In our criterion, the coverage of a test set is measured as the expected amount of entropy decrease (i.e. the expected amount of information gained) once the test set is run. Since our criterion is syntactic independent, we study the notion of information-optimal software testing where, within a given constraint, a test set is selected to gain the most information.
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Details
- Title
- Information gain of black-box testing
- Creators
- Linmin Yang - School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164 USAZhe Dang - School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164 USAThomas Fischer - School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164 USA
- Publication Details
- Formal aspects of computing, Vol.23(4), pp.513-539
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Springer-Verlag; London
- Identifiers
- 99900547141101842
- Language
- English
- Resource Type
- Journal article