Reduction and Slicing of Hierarchical State Machines Mats Heimdahl et al. University of Minnesota Presented by Tom McMullen For CISC836 1.

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Reduction and Slicing of Hierarchical State MachinesMats Heimdahl et al.

University of Minnesota

Presented by Tom McMullenFor CISC836

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Outline

• Primer • Problem Space• Paper Overview• Application (Case Study)• Limitations• Critique• Discussion

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Primer

• Presented at 1997 Proceedings 6th European Software Engineering Conference

• Cited by 65 (source: Google Scholar)

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Problem Space

• Formal Specification Languages– Difficult to understand and use– Not well understood by application experts

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Problem Space

• State Machine Representation– Better, but…

Inevitable Complexity for large systems

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Problem Space

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What this paper proposes…

• Address complexity of HSMs• In Order To:– Present information in digestible chunks

• Method:– Step 1: Simplify based on scenario– Step 2: Slice for desired values

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A Quick Refresher…

• Hierarchical State Machines (HSM)

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A Quick Refresher…

• Slicing

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A quick refresher…

• Program Slicing

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Application

• Applied to HSMs– RSML (Requirements State Machine Language)• Specification of safety-critical systems

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RSML

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Testbed Specification

• TCAS II– Traffic alert and Collision Avoidance System

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Step 1: Reduce

• Produce simplified RSML model• Interpretation based on scenario– Domain restriction of next-state relation– How do we classify an intruder who has stopped

reporting altitude?

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Step 1: Reduce

• Eliminate infeasible columns for scenario• Reduction Algorithm limited to enumerated vars

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Step 2: Slice

• Slicing Algorithm– Based on marking of Abstract Syntax Tree– RSML parser part of earlier research

• Data Flow Slices (if a transition can be taken)– Data Dependency of Guarding Transition

• Control Flow Slices (when a transition is taken)– Generation of a trigger event

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Step 3: Profit?...

• Iterative slices are then combined• Attempt to answer our questions• Reduced complexity• Increased Understandability

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Case Study

• Applied to most complex part of TCAS II RSML Model

• Subjective notion of complexity / understandability

• Metrics:– # of transitions– Perceived Table Size– Effective Table Size

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Case Study

• Results– Promising• Significant reduction in table size

– But..• Reduction in # of transitions not as expected

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Critique

• Case Study details lacking– Which TCAS model? What Questions/Scenario?– 1998 paper has a more detailed case

study/empirical data• Too much emphasis on RSML– Small application– More detail on slicing HSM in general

• Reduction and Slicing algorithms not presented– Data flow / control flow slices not explained in depth

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Discussion