Software Agent Testing - FBK | SE - Fondazione Bruno Kessler

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Random generation• Messages:‣ select randomly a standard interaction protocol, combine itwith randomly-generated data or domain specific data• Environmental settings:‣ identify the attributes of theentities in the environmentExample:‣ generate randomly values forthese attributes28
Ontology-based generation• Information available inside interaction ontology:‣ concepts, relations, data types of properties. E.g. action Proposeis an <strong>Agent</strong>Action, two properties: book: Book , price: Double‣ instances of concepts, user-defined or obtained from ontologyalignment• Use these data to generate messages automaticallyExample:Book(title:”A”)Propose(book:”A”, price:10)tester agentPropose(book:”A”, price:9)agent under test29
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 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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