Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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Quality-function-based evolutionarygeneration• Build the fitness of test cases based on quality functions• Use this fitness measure to guide the evolution, using agenetic algorithm, e.g GA, NSGAII etc.• For example:‣ soft-goal: safety‣ quality function: the closest distance of the agent to obstaclesmust be greater than 0.5cm‣ fitness f = d - 0.5, search for test cases that give f < 0generation idistancegeneration i + Kdistance0.50.5timetime30
Example: evolutionary testing of the cleaneragentn Test case (environment)encoding¨ coordinates (x,y) ofwastebins, charging stations,obstacles, wastes¨ TCi = n Fitness functions:¨fpower = 1/Total power consumptionnsearch for environments where the agent consumesmore power¨fobs = 1/Number of obstacles encounterednsearch for environments where the agent encountersmore obstacles31
Page 1 and 2: Testing SoftwareAgents & MASCu D. N
Page 3 and 4: Software agents and MASSoftware age
Page 5 and 6: Testing phasesn Acceptance: ensure
Page 7 and 8: Some factsn Many BDI agent dev. lan
Page 9 and 10: Testing agent beliefsn Belief state
Page 11 and 12: Testing agent plansn Plans are trig
Page 13 and 14: Example: testing a cleaning agentn
Page 15 and 16: Example (contd.)n Example of goal$b
Page 18 and 19: Ontology-based oraclesn Interaction
Page 20 and 21: Input space in testing agentsn Test
Page 22 and 23: Mock Agentsn Mock Agents are sample
Page 24 and 25: Continuous Testing ofAutonomous Age
Page 26 and 27: Continuous testingn Consists of inp
Page 28 and 29: Random generation• Messages:‣ s
Page 32 and 33: Example (contd)n Genetic algorithm,
Page 34 and 35: Conclusionsn Testing software agent

Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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