Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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Example (contd)n Genetic algorithm, driven by fpower and fobs, will searchfor test cases in which the agent will have higherchance to run out of battery, and hit obstacle.¨ that violates the user’s requirementsnResults:nEvolutionary test generationtechnique founds test caseswhere:1) wastes are far away fromwastebin -> more power2) obstacles on the way towastes -> easy to hitBlack circles: obstaclered dots: wastes - squares: charging stations, red circles: wastebins32
Example (contd)n More about the evolutionary of theenvironment:¨ http://www.youtube.com/watch?v=xx3QG5OuBz0n The search converts to the test cases wherethe 2 fitness functions are optimized.33
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 30 and 31: Quality-function-based evolutionary
Page 34 and 35: Conclusionsn Testing software agent

Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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