Model-based Test Generation for Robotic Software: Automata versus
Belief-Desire-Intention Agents
release_gaepn6gr5vb6zacm6b542ranzq
by
Dejanira Araiza-Illan, Anthony G. Pipe, Kerstin Eder
2016
Abstract
Robotic code needs to be verified to ensure its safety and functional
correctness, especially when the robot is interacting with people. Testing real
code in simulation is a viable option. However, generating tests that cover
rare scenarios, as well as exercising most of the code, is a challenge
amplified by the complexity of the interactions between the environment and the
software. Model-based test generation methods can automate otherwise manual
processes and facilitate reaching rare scenarios during testing. In this paper,
we compare using Belief-Desire-Intention (BDI) agents as models for test
generation with more conventional automata-based techniques that exploit model
checking, in terms of practicality, performance, transferability to different
scenarios, and exploration (`coverage'), through two case studies: a
cooperative manufacturing task, and a home care scenario. The results highlight
the advantages of using BDI agents for test generation. BDI agents naturally
emulate the agency present in Human-Robot Interactions (HRIs), and are thus
more expressive than automata. The performance of the BDI-based test generation
is at least as high, and the achieved coverage is higher or equivalent,
compared to test generation based on model checking automata.
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