An Integrated Framework for Validating Model-based Embedded Software Li Tan

1. An Integrated Framework for Validating Model-based Embedded Software Li Tan University of Pennsylvania September, 2003 Testing and Monitoring at Penn

2. Outline • Motivations • The overview of our approach • Model-based monitoring. • Monitoring hybrid automata • From logic specification to model-based monitor • Model instrumentation • Model-based testing • Creating and refining model-based testers • The case study on SONY AIBO Dog • Design-level validation • “on-board” validation • Conclusion Testing and Monitoring at Penn

3. Motivations • Implementing state-of-art validation technique is a painful and costly process. • Validation requires special instrument/programs. • The details/interfaces of targeted tools are not always available. • Lack of an integrated solution for both design-level and implementation-level validations. • Difficult to relate the result of design-level validation with implementation-level validation. • Bringing formal methods techniques down to implementation level is challenging. • Validation directly on targeted hardware platform is much needed in designing model-based embedded software. Testing and Monitoring at Penn

4. The outline of our approach • Goal: introducing state-of-art validation techniques to model-based software design domain using existing tools and techniques. • Solution: a model-based validation technique for model-based software design. • A four-step plan. • Synthesizing model-based monitor from the logic specification. • Creating model-based tester from the testing criteria. • Design-level validation: simulating self-monitoring and self-testing model. • Implementation-level validation: generating self-monitoring and self-testing code for target hardware from the composed model. Testing and Monitoring at Penn

5. System model Tester Model Instrumented model Monitor Model Design Level Validation Composed Model Simulator CodeGenerator Self-testing and Self-monitoring Code Implementation Level Validation An overview Coverage Criteria/ environment constrains MEDLspecification Model Instrumentation Monitor Synthesizer Coverage Checker Testing and Monitoring at Penn

6. The details of our plan • Modeling language: Charon for hybrid systems. Charon toolkit has, • A simulator. • A code generator to C++. • Model-based testing and monitoring. • Testing: working well on implementation level. • Need be extended to model level • Runtime verification: checking the execution of software system. • Need be extended to hybrid systems • SONY AiBo Robot Dog: a hands-on example. • The controller for its head is generated from a Charon model. Testing and Monitoring at Penn

7. Outline • Motivations • Overview of our approach • Model-based Monitoring. • Monitoring hybrid automata • From logic specification to model-based monitor • Model instrumentation • Model-based Testing • Creating and refining model-based testers • Case study on SONY AIBO Dog • Design-level validation • “on-board” validation • Conclusion Testing and Monitoring at Penn

8. Runtime verification Runtime verification (monitoring) is to check an execution of a program again its temporal logic specification Java Programs MEDLspecification MEDL compiler Program Instrumentation Monitoring script events Java Programs +Filter Monitor Computer Testing and Monitoring at Penn

9. System Automaton System Automaton Filter Monitoring Automaton Monitoring hybrid automata MEDLspecification Model Instrumentation Medl2Charon Monitor Synthesizer Instrumented Model Simulator Testing and Monitoring at Penn

10. Hybrid Automata • A hybrid automaton A={S, V, T, G, W, D, A, I, s0} extends a EFSM {S, V, T, G, W, s0} with continuous behaviors • S is the set of modes • V is the set of variables • T=V£ V is the set of transitions • G assigns each t 2 T a guard, a predicate over V. • W assigns each t 2 T an assignment for Vµ V. • D assigns each s 2 S a set of differential equations over . • A assigns each s 2 S a set of algebra equations over V. • I assigns each s 2 S an invariant, a predicate over V. • s02 S is the initial mode. Testing and Monitoring at Penn

11. An example: Dog Head Controller • Hybrid automata can be composed concurrently. • Hybrid automata can be composed hierarchically. • A location can be a collection of sub-locations. || >10? x 45? x  . x = -10 x -46 x -45 : The visibility of the ball x: The angle of the head : The angle of the ball ||  10? Testing and Monitoring at Penn

12. MEDL: expressing your properties • MEDL (Meta Events Definition Language) is a linear interval temporal logic for specifying safety properties. • MEDL is initially introduced for monitoring Java programs in MaC (Monitor and Checking) System [KKL01] • Syntax • Defined on conditions, events, and expressions. C := defined(C) | [E, E) | : C | C && C | C||C | Q  Q E := e | start(C) | end(C) | E || E | E&&E | E when C Q := time(E) | c |Q }Q Where e is primitive event, c is a constant, 2 {>, <, =}, and }2 {*,/,+,-} . Testing and Monitoring at Penn

13. (Informal) MEDL semantics Interpreting MEDL on runs of hybrid automata, • A condition C maps a time period with true, false, or undefined. If C is, • [E1, E2). C is true from event E1 to E2 (not included) • C1&&C2. C is true when C1 and C2 are both true. • C1||C2. C is true when either of C1 and C2 is true. • : C1. C is the dual of C1. • An event E maps a time instance with true or false. If E is, • start(C). E occurs at the time C becomes true. • end(C). E occurs at the time C becomes not true. • E1||E2. E occurs when either E1 or E2 occurs. • E1&&E2. E occurs when both E1 and E2 occur. • E1 when C. E occurs when E1 occurs and C is true • Q is an expression. If Q is, • time (E). Q’s value is the latest time E occurs. • Q1} Q2. Q’s value is Q1} Q2. . Testing and Monitoring at Penn

14. An example • If the dog loses the ball 50 seconds after the ball becomes visible, an alarm should be raised. Begin import event isVisible, isInvisible, track,lost; condition visible= [isVisible, isInvisible); event becameTruelost= lost when visible; alarm lostTrack=start (time(becameTruelost)-time(isVisible)>50); End • isVisible, isInvisible, track, and lost are primitiveevents • isVisible (isInvisible) occurs when  >10 becomes true (false). • Lost (track) occurs when |-x| > 10 become true (false). • An alarm is an event which indicates the violation of the safety requirement. . Testing and Monitoring at Penn

15. From Spec to Monitor • Monitoring hybrid automaton A is synthesized from its MEDL specification S, • For each event E in S, • Variable VE in A records the latest time E occurs. • For each condition C in S • Variable VC in A records the current value of C. • Variable VC- in A records the previous value of C. • Variable VCl records the latest time C changes. • For each expression Q in S • Variable VQ in A records the value of Q. . Testing and Monitoring at Penn

16. The monitor-synthesizing algorithm (1) The translation is highly modularized, • Each condition, expression, and event is translated to an automaton just for it. • Each has a token ID Pc reflecting its syntactical order in MEDL script. • Each automaton is enabled only if the token is passed to it (P=Pc) • t records the occurrence timeof the primitive event being processed. . (a) C=: C1 Testing and Monitoring at Penn

17. The monitor-synthesizing algorithm (2) (b) C=[E1, E2) . (c) E=start(C) (d) Q=time(E) Testing and Monitoring at Penn

18. Monitoring Automaton Automaton e1 Automaton Cn Engine Automaton The monitor-synthesizing algorithm (3) • Monitor is the composition of engine automaton and the automata for events, conditions, and expression. • Engine automaton checks incoming event and initialize the token. .  Event, Condition, and Expression Automaton  Token flow • Alarm/event detecting is indicated by the value change on the event variable. • VE records the time E occurs in the model. Testing and Monitoring at Penn

19. Outline • Motivations • Overview of our approach • Model-based Monitoring. • Monitoring hybrid automata • From logic specification to model-based monitor • Model instrumentation • Model-based Testing • Creating and refining model-based testers • From Simulation to Implementation • Case study on SONY AIBO Dog • Design-level validation • “on-board” validation • Conclusion Testing and Monitoring at Penn

20. Model Instrumentation (1) Monitor observes primitive events emitted by system model. • Event is emitted via shared variables • (Option I) Model modification. || >10? x 45? x -45 ||  10? Testing and Monitoring at Penn

21. Model Instrumentation (1) Monitor observes primitive events emitted by system model. • Event is emitted via shared variables • (Option I) Model modification. || >10?VisVisible=t, newEvent:=true |-x| 10?Vtrack=t, newEvent=true x 45? x -45 |-x| >10?Vlost=t, newEvent=true ||  10?VisInvisible=t, newEvent=true Testing and Monitoring at Penn

22. Model Instrumentation (2) • (Option 2) Model Augmentation • A observer automaton (filter) is concurrently composed with the model. • The structure of the model will not be changed. || >10? Filter x 45? |-x| >10?Vlost=t,newEvent=true || >10?VisVisible=t,newEvent=true |-x|10?Vtrack=t,newEvent=true ||10?VisInvisible=t,newEvent=true x -45 ||  10? Testing and Monitoring at Penn

23. Outline • Motivations • Overview of our approach • Model-based Monitoring. • Monitoring hybrid automata • From logic specification to model-based monitor • Model instrumentation • Model-based Testing • Creating and refining model-based testers • Case study on SONY AIBO Dog • Design-level validation • “on-board” validation • Conclusion Testing and Monitoring at Penn

24. Environ. Model System Model Tester Model Modeling testing task Tester model reassembles a virtual environment which supplies test trace Environment /hardware constrains + Simulator No Determination Coverage Criteria Coverage Checker Yes Test Trace Testing and Monitoring at Penn

25. Modeling tester: an example (1) • Testing requirements. • Testing should cover all the locations in system model. • Testing should check the dog’s behavior when it lose the ball. • Step I: Modeling the environment as a non-deterministic hybrid automaton. true?=0 true?d=d-10 true?d=d+10 =0 =0 true?b=b+0.1 true?b=b-0.1 true?a=a+0.005 true?a=a-0.005 true?=20,t=0 Testing and Monitoring at Penn

26. Modeling tester: an example (2) • Step II: Select a simulation trace as test case.   time • Step III: Determinate environ. model for the test case u=10?=20,t=0,a=0.025,b=0.2,d=90 Testing and Monitoring at Penn

27. Outline • Motivations • Overview of our approach • Model-based Monitoring. • Monitoring hybrid automata • From logic specification to model-based monitor • Model-based instrument • Model-based Testing • Create environment model • Obtain model-based tester model from environment model • Case study on SONY AIBO Dog • Design-level validation • “on-board” validation • Conclusion and related works Testing and Monitoring at Penn

28. Design-level validation • The composition of instrumented model, tester, and monitor forms a self-validating Charon model • Tester supplies test trace during simulation • The occurrence of an event is indicated by the value of event variables during simulation (a) Alarm Detection Testing and Monitoring at Penn

29. Design-level validation (b) Primitive Events Emitted by Instrumented Model (c) The simulation trace of monitor Testing and Monitoring at Penn

30. Implemtation-level validation Monitoring automaton System Model Testing automaton Modular compilation Monitor Generated Code Tester Link as needed Testing and Monitoring at Penn

31. Implementation-level validation • The alarm is used for calling the external functions to report error. • “play” function is called when an alarm lostTrack is detected • The space overhead of tester and monitor. Testing and Monitoring at Penn

32. Conclusion We proposed a framework for testing and monitoring model-based embedded systems. • The approach works directly on models. • Monitor and tester tasks are specified in the high-level modeling language. • It doesn’t require the changes on simulator etc. • The framework is for both design-level and implementation-level validations. • The results on implementation-level validation may be linked to design-level validation. • It produces a self-testing and self-monitoring code for embedded system • Monitor and tester are executable on the targeted hardware platform • Validation is done directly on “board”. Testing and Monitoring at Penn

33. Thank you! Testing and Monitoring at Penn