Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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Ontology-based oraclesn Interaction ontology defines the semantics of agentinteractionsn Mismatching ontology specifications is faulty<strong>Agent</strong>ActionProposeThingConcept+book: Book+price: floatBook+title: String+author: String+ title: string+ price: doubleFig. 2. The book-trading interaction ontology, specified as UML class diagramRule examplemin 0 and max 2000In the course of negotiation, a buyer initiates the interaction by sending acall for proposals for a given book (an instance of Book) to all the sellers that18
Requirement-based (stakeholder soft-goalsbased)n Soft-goals capture quality requirements, e.g. performance,safetyn Soft-goals can be represented as quality functions (metrics)n In turn, quality functions are used to assess the agentsunder testEfficientRobustεd > εtimeGood lookingtime19
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 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 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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