1. System Modelling and Verification
The lecture contains material from Lothar Thiele, ETH Zurich

2. Processing system are everywhere and they are highly inter-connected
Introduction
Processing system are everywhere and they are highly inter-connected
ABS
gear box
motor control
climate control
entertainment
Kai Lampka

3. Introduction → high degree of non-determinism
Systems are distributed and loosely coupled
→ high degree of concurrency
Large degree of uncertainty w.r.t. timing and interaction
→ high degree of non-determinism
Systems need to fulfill a set of (quantifiable) constraints, e.g. given in TCTL
Kai Lampka 3

4. Modelling and Analysis
System complexity can not be grasped by human- beings, at least as a whole, see PI-Problem.
How does one ensure that a system design is free of systematic errors and fulfills its requirements?
Examples: Reactivity within time bound, Deadlock- freeness, Buffer does not over-/underflow, absence of PI, timing correctness…..
Need for scalable analysis methods of ensure that system designs satisfies predefined properties. implementation and analysis methods!

5. Verification & Validation
System Engineering with the V-process
Engineering = Design and Implementation + Deployment
Verification & Validation
Concept of Operations
Operations & Maintenance
System Verification & Validation
Requirements & Architecture
Verification
&
Validation
Integration, Test & Verification
Detailled Design
Source: US department of transportation (see also wikipedia.org)
Implementation
Time
For avoiding mal-developments and costly re-design of existing systems Verification, Validation, and Testing has to be integrated into the design process as early as possible!

6. Methodologies for evaluating System Designs
Empirical Methods
Deductive Methods
Model-based
Real System (Prototype)
State-based methods: behaviour is captured by finite graphs,
Examples: PN, StateCharts
Simulation: behaviour is evaluated by statistics over individual runs (some snap-shots)
Measurement, Monitoring
Testing
Kai Lampka
Analytic methods: behaviour is deduced from closed-form formulae.
Example: Process Networks, PN
Industrial practice
Non-exhaustive
Exhaustive

7. Requirements for Modeling technique
Represent hierarchy
Humans not capable to understand systems containing more than a few objects, particularly when here is feedback/complex interaction
Most actual systems require more objects
Hierarchy of objects
Behavioral hierarchy
Examples: states, processes, procedures.
Structural hierarchy
Examples: processors, racks, printed circuit boards

8. Requirements for Modeling Techniques (2)
Represent timing behavior/requirements
Represent state-oriented behavior suitable for reactive systems and complex behavior of SW.
Represent dataflow-oriented behavior Components send streams of data to each other.
No obstacles for efficient implementation, of the analysis methods and the system (synthesis of skeletons)

9. Models of Computation: Definition
What does it mean, “to compute”?
Models of computation define:
Components and an execution (semantic) model for computations for each component, e.g., Token-game for PN)
Communication model for exchange of information between components (semantic of interaction synchronous/asynchronous)
Shared memory
Message passing …

10. Semantic of communication: Shared memory
Potential race conditions (inconsistent results possible)
Communication must be implemented as critical section (sections at which exclusive access to resource r (e.g. shared memory) must be guaranteed).
process a { P(S) //obtain lock
.. // critical section V(S) //release lock }
process b { P(S) //obtain lock
.. // critical section V(S) //release lock }
Race-free access to shared memory protected by S possible

11. Semantic of communication: Non-blocking/asynchronous message passing
Sender does not have to wait until message has arrived; potential problem: buffer overflow, e.g., PN without inhibitor arcs …
send () …
receive ()

12. Semantic of communication: Blocking/synchronous message passing
Sender will wait until receiver has received message, e.g., joint execution of transitions in PN (transitions are merged according to an logical AND) …
send () …
receive ()

13. Semantic of communication: Synchronous message passing: CSP
CSP (communicating sequential processes) [Hoare, 1985], rendez-vous-based communication.
process A ..
var a ...
a:=3;
c!a; -- output action
end
process B ..
var b ...
...
c?b; -- input action
end
This basic mechanism can be found in most automata- based modelling formlisms, e.g., Timed Automata of Uppaal.
Modeling asynchronous behaviour by explicitly modeling communication media (Queue)

14. Semantic of computation
Discrete State Systems
Finite state machines
Petri Nets
Continuous State Systems
Differential equations
Hybrid (continous states/ discrete control states)
Timed Automata

15. Model of computation No language that meets all language requirements
Use-case give needs and determines capabilities required from the modeling technique
But, remember:
Computation effort to do analysis differs considerably!
Extension of Formalisms: Small changes in the modeling technique can easily result in undecidability for deciding state reachability!

16. StateCharts
Classical automata not useful for complex systems (complex graphs cannot be understood by humans).
Introduction of hierarchy
StateCharts [Harel, 1987]
in parts re-used in UML

17. Introducing Hierarchy
FSM will be in exactly one of the substates of S if S is active (either in A or in B or ..)

18. Definitions Current states of FSMs are also called active states.
States which are not composed of other states are called basic states.
States containing other states are called super-states.
For each basic state s, the super-states containing s are called ancestor states.
Super-states S are called OR-super-states, if exactly one of the sub-states of S is active whenever S is active.
superstate
ancestor state of E
substates

19. Default State Mechanism
Filled circle indicates sub-state entered whenever super-state is entered.
Entrance point, not a state by itself!

20. Saving history
(behavior different from last slide) m k
For input m, S enters the state it was in before S was left (can be A, B, C, D, or E). If S is entered for the very first time, the default mechanism applies.
History and default mechanisms can be used hierarchically.

21. Combining History and Default State
same meaning

22. Concurrency Convenient ways of describing concurrency are required.
AND-super-states: FSM is in all (marked) sub- states of a super-state.

23. Entering and Leaving AND-Super-States
incl.
Line-monitoring and key-monitoring are entered and left, when service switch is operated.

24. Tree representation of state sets
OR-super-state
AND-super-state
basic state A B E C D F M G H I K L A C D B E F I K L M G H A X Y B C Y Z X A

25. Computation of state sets
Computation of state sets by traversing the tree top-down
basic states: state set = state
OR-super-states: state set = union of children
AND-super-states: state set = Subset of cartesian product of children A B E C D F M G H I K L

26. Types of States In StateCharts, states are either Basic states, or
AND-super-states, or
OR-super-states.

27. Timers
Since time needs to be modeled in embedded systems, timers need to be modeled.
In StateCharts, special edges can be used for timeouts.
If event a does not happen while the system is in the left state for 20 ms, a timeout will take place.

28. Using Timers: Example of an answering Machine

29. Extension of sematic to variables
Besides states, arbitrary many other variables can be defined. This way, not all states of the system are modeled explicitly.
These variables can be changed as a result of a state transition (“action”).
State transitions can be dependent on these variables (“condition” ).
action
unstructured state space
variables
condition

30. Syntax: General Form of Edge Labels
event [condition] / action
also called guard
Events:
Exist only for the next evaluation of the model
Can be either internally or externally generated
Conditions: Refer to values of variables that keep their value until they are reassigned
Actions: Can either be assignments for variables or creation of events
Example: service-off [a <= 7] / service:=0

31. Events and actions “event” can be composed of several events:
(e1 and e2) : event that corresponds to the simultaneous occurrence of e1 and e2.
(e1 or e2) : event that corresponds to the occurrence of either e1 or e2, or both.
(not e): event that corresponds to the absence of event e.
„action“ can also be composed:
(a1; a2) : actions a1 und a2 are executed in parallel.
Note: Events, states and actions are globally visible!

32. Example true false true false x y z e/a1 [c]/a2 e: a1: a2: c: e: a1:

33. StateChart Model execution Phases
How are edge labels evaluated?
Three phases:
1. Effect of external changes on events and conditions is evaluated,
2. The set of transitions to be made in the current step and right hand sides of assignments are computed,
3. Transitions become effective, variables obtain new values.

34. Example
In phase 2, variables a and b are assigned to temporary variables. In phase 3, these are assigned to a and b. As a result, variables a and b are swapped.
In a single phase environment, executing the left state first would assign the old value of b (=0) to a and b. Executing the right state first would assign the old value of a (=1) to a and b.
=> Execution is non-deterministic, one needs to consider all permutations.

35. Model of compuation
State Space exploration (step-wise execution) of a StateChart model consists of a sequence of (status, step) pairs
Status= values of all variables + set of events + current time (state)
Step = execution of the three phases (state-to-state transition)
Status
phase 2
phase 3
phase 1

36. Motivation for this modus operandi:
Motivation for this modus operandi: It reflects model of clocked hardware
In an actual clocked (synchronous) hardware system, both registers would be swapped as well.
Same separation into phases found in other languages as well, especially those that are intended to model hardware (e.g., synchronous languages, LUSTRE).

37. Alternative interpretation
Unfortunately, there are several (synchronous) time-semantics for StateCharts available.
This is another possibility:
A step is executed in arbitrarily small time.
Internal (generated) events exist only within the next step.
Difference: External events can only be detected after a stable state has been reached.
external events
stable state
stable state
state
transitions t
transport of internal events
step

38. Example
state diagram:
stable states

39. Example
state diagram (only stable states are represented, only a and b are external): A D E C F H
c/d d d a B
a b  a b _ a _ G I
a b
G,H b _ b
a b B
a/c
a b  a b _
F,H

40. Example Non-determinism a a A C E G a a B D F H state diagram: A,B C,D
E,H
F,G a

41. Evaluation of StateCharts (1)
Pros:
Hierarchy allows arbitrary nesting of AND- and OR- super states.
Semantics defined in a follow-up paper to original paper.
Large number of commercial simulation tools available (StateMate, StateFlow/Matlab, BetterState, UML, ...)
Available „back-ends“ translate StateCharts into C or VHDL, thus enabling software or hardware implementations.

42. Evaluation of StateCharts (2)
Cons:
Generated C programs frequently inefficient,
Not useful for distributed applications,
No description of non-functional behavior,
No object-orientation,
No description of structural hierarchy.

43. SDL
Specification and Description Language (SDL) is a specification language targeted at the unambiguous specification and description of the behaviour of reactive and distributed systems. Used here as a (prominent) example of a model of computation based on asynchronous message passing. Appropriate also for distributed systems

44. Communication of SDL-FSM
Communication between FSMs (or “processes“) is based on message-passing, assuming a potentially indefinitely large FIFO-queue.
Each process fetches next entry from FIFO,
checks if input enables transition,
if yes: transition takes place,
if no: input is discarded (exception: SAVE-mechanism).

45. Deterministic? Let tokens be arriving at FIFO at the same time.
Order in which they are stored, is unknown
All orders are legal: simulators can show different behaviors for the same input, all of which are correct.