Using mutual information to test from Finite State Machines: Test suite generation
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- @Article{IBIAS:2022:jss,
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author = "Alfredo Ibias",
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title = "Using mutual information to test from Finite State
Machines: Test suite generation",
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journal = "Journal of Systems and Software",
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volume = "192",
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pages = "111391",
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year = "2022",
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ISSN = "0164-1212",
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DOI = "doi:10.1016/j.jss.2022.111391",
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URL = "https://www.sciencedirect.com/science/article/pii/S0164121222001108",
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keywords = "genetic algorithms, genetic programming, Formal
approaches to testing, Information Theory, Mutual
information, Finite State Machines",
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abstract = "Mutual Information is an information theoretic measure
designed to quantify the amount of similarity between
two random variables ranging over two sets. In recent
work we have use it as a base for a measure, called
Biased Mutual Information, to guide the selection of a
test suite among different possibilities. In this
paper, we adapt this concept and show how it can be
used to address the problem of generating a test suite
with high fault finding capability, in a black-box
scenario and following a maximise diversity approach.
Additionally, we present a new Grammar-Guided Genetic
Programming Algorithm that uses Biased Mutual
Information to guide the generation of such test
suites. Our experimental results clearly show the
potential value of our measure when used to generate
test suites. Moreover, they show that our measure is
better in guiding test generation than current
state-of-the-art measures, like Test Set Diameter
(TSDm) measures. Additionally, we compared our proposal
with classical completeness-oriented methods, like the
H-Method and the Transition Tour method, and found that
our proposal produces smaller test suites with high
enough fault finding capability. Therefore, our
methodology is preferable in an scenario where a
compromise is necessary between fault detection and
execution time",
- }
Genetic Programming entries for
Alfredo Ibias
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