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WP3: Qualitative Fault Modelling

WP3: Qualitative Fault Modelling. András Pataricza, Professor Budapest University of Technology and Economics. Qualitative Fault Modelling – Objectives. Exploratory study for test optimization

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WP3: Qualitative Fault Modelling

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  1. WP3: Qualitative Fault Modelling András Pataricza, ProfessorBudapest University of Technology and Economics

  2. Qualitative Fault Modelling – Objectives Exploratory study for test optimization Identification of fault classes that have significant effects regarding dependability/safety requirements Based on systematic modelling of faults Addressing the model complexity problem Qualitative abstraction: Aggregating states/values belonging to the same operational domain Spatial abstraction: Using error predicates Temporal abstraction: Using temporal predicates Semi-decision supported by the abstract model Negative result is a proof of non-existence of critical faults Positive result shall be checked in the concrete model(TCG controlled by the analysis in the abstract model)

  3. Qualitative Fault Modelling – Progress Demonstrating theabstraction method: Modelling reference instance and mutations (failure modes) Construction of composite automata Signal level spatialcompaction Temporal compaction Demonstrating system level analysis Network of relations Mapping to ConstraintSatisfaction Problem

  4. Qualitative Fault Modelling – Results and plans Feasibility study finished (D3.1b) Modelling a Car Alarm System Abstraction by manual steps Spatial and temporal abstraction Syndrome level static modelling Mapping to CSP using tools Solution by CSP solver +model checker Results and plans Potentials of the approach were demonstrated Guiding heuristics for test generation (reducing search space) Supporting diagnostics Application conditions were identified Target models: Networks of interconnected components Automated abstraction: Elaboration of tool support would need more resources

  5. WP3: Ontology-based Model Verification András Pataricza, ProfessorBudapest University of Technology and Economics

  6. Ontology Based Verification – Objectives • Verification of application specific models to have • well-defined, • consistent, • complete models, • which meet some modelling constraints. These application models are the inputs to test case generation.

  7. Ontology Based Verification – Progress • Deliverable 3.3a – Ontology based model verification (M18) • First version • Identification of modelling constraints • Theory of ontology based verification • Application of it in MOGENTES • verification of the application model as a UML model • verification of the application model as a domain model • verification of instance models with respect to the application models • Verification of process models Done

  8. Examples of Constraints That are Checked • Class Diagram related • Consistency, coherence • State machine diagram exists for all active classes • Coverage of all defined input and other non-output signals by at least one transition trigger • State Machine related • Each state is targeted by at least one transition • State machines are deterministic • Behavior related • Sufficient method definition • Find unused methods • Application related • Existence of a marked up singleton class representing the system itself

  9. Ontology Based Verification–Implementation

  10. Ontology Based Verification–Framework Process Implementation Step 1: Transformation Step 2: Execution of verification

  11. Ontology Based Verification – Planning • Deliverable 3.3b – Ontologybasedmodelverification (M30) • Improved version of D3.3a • Identification of new modelling constraints • basedon modelling experiences • Verification of finalMogentesdemonstratormodels • Improvementof theontology-basedmodelverificationtool

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