Information gain of black-box testing

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 s...

Full description

Saved in:
Bibliographic Details
Published in:Formal aspects of computing 2011-07, Vol.23 (4), p.513-539
Main Authors: Yang, Linmin, Dang, Zhe, Fischer, Thomas R.
Format: Article
Language:English
Subjects:
Adequacy
Applied sciences
Computer programs
Computer Science
Computer science
control theory
systems
Criteria
Entropy
Exact sciences and technology
Gain
Math Applications in Computer Science
Miscellaneous
Original Article
Random variables
Software
Specifications
Theoretical computing
Theory of Computation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0934-5043
1433-299X
DOI:10.1007/s00165-011-0175-6