Papers 2020
Optimizing the Parameters of an Evolutionary Algorithm for Fuzzing and Test Data Generation
Date | 23 March 2020 |
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Event | 3rd IEEE Workshop on NEXt level of Test Automation 2020 (NEXTA 2020) |
Location | Porto, Portugal |
Communication interfaces are particularly
challenging to test using automatically generated test
data. The test data sent through the interface must be
”valid enough” to overcome initial sanity checks of the
interface and reach functions deep inside the integrated
software. Machine-readable information about what
data forms ”valid enough” messages is rarely available
to test data generation tools. So instead, we evolve the
messages with an evolutionary algorithm. This enables
efficient fuzz testing for the communication interface
between a satellite and its ground station. In this pa-
per, using an algorithm implementation in our fuzzing
tool DCRTT, we investigate the impact of algorithm
parameter selection on the performance and the possi-
bility of efficient general default parameter values. The
preliminary results promise significant improvements
to automated testing with respect to software security
testing and quality assurance.
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