The document discusses the challenges and methodologies related to context-aware adaptive applications, specifically focusing on fault detection and adaptation mechanisms. It presents statistics from the Google Android market, highlighting issues with low-quality apps, and details various faults and hazards associated with context adaptations like nondeterminism and unreachable states. Future work includes exploring alternatives to rule-based adaptation and employing machine learning for context classification.

1. High-Confidence
Ubiquitous Computing Systems
David S. Rosenblum
School of Computing
National University of Singapore

2. Some Facts
Google Android Market
• The average price of the top 50 paid
applications is US$3.79 [modymi.com]
• 79.3% of paid applications have been
downloaded less than 100 times [Distimo]
• Only 0.1% of paid applications have been
downloaded 50,000 times or more [Distimo]

3. Some Facts
Google Android Market
• The average price of the top 50 paid
applications is US$3.79 [modymi.com]
• 79.3% of paid applications have been
downloaded less than 100 times [Distimo]
• Only 0.1% of paid applications have been
downloaded 50,000 times or more [Distimo]
There are many simplistic, low-quality apps!

4. CAAAs
Context-Aware Adaptive Applications

5. CAAAs
Context-Aware Adaptive Applications

6. CAAAs
Context-Aware Adaptive Applications

7. CAAAs
Context-Aware Adaptive Applications

8. CAAAs
Context-Aware Adaptive Applications

9. Adaptation in CAAAs
Application
Environment

10. Adaptation in CAAAs
Application
Physical Context Environment

11. Adaptation in CAAAs
Application
Adaptation
Middleware
Manager
Context
Manager
Physical Context Environment

12. Adaptation in CAAAs
Application
Adaptation
Middleware
Manager
Context
Sensed Context Manager
Physical Context Environment

13. Adaptation in CAAAs
Application
Adaptation
Middleware
Inferred Context Manager
Context
Sensed Context Manager
Physical Context Environment

14. Adaptation in CAAAs
Presumed Context Application
Adaptation
Middleware
Inferred Context Manager
Context
Sensed Context Manager
Physical Context Environment

15. Adaptation in CAAAs
Presumed Context Application
Adaptation
Middleware
Inferred Context Manager
Context 3rd-Party
Sensed Context Manager Libraries
Physical Context Environment

16. Adaptation in CAAAs
Presumed Context Application
Adaptation Rule
Middleware
Inferred Context Manager Engine
Context 3rd-Party
Sensed Context Manager Libraries
Physical Context Environment

17. Validation of CAAAs
Application
Adaptation Rule
Middleware
Manager Engine
Context
Manager
Environment

18. Validation of CAAAs
Rules are strongly
interdependent Application
and have multiple
priorities Adaptation Rule
Middleware
Manager Engine
making reasoning
Context
difficult even for a Manager
small number of rules
Environment

19. Validation of CAAAs
Application
Adaptation
Middleware
Manager
Context 3rd-Party
Manager Libraries
Environment

20. Validation of CAAAs
Context is sensed
periodically Application
from multiple Adaptation
Middleware
Manager
sources
Context 3rd-Party
at varying rates Manager Libraries
Environment

21. Approach
1. Derive Adaptation Finite-State Machine (A-
FSM) from rule logic
2. Explore state space of A-FSM to discover
potential faults
✓ Enumerative algorithms
✓ Symbolic algorithms
✓ Planner-based counterexample generation
3. (Confirm existence of discovered faults)

22. PhoneAdapter

23. PhoneAdapter
silent, vibrate
loud,
normal, divert to
vibrate hands-free
silent, divert to voicemail loud, vibrate

24. PhoneAdapter
silent, vibrate
loud,
normal, divert to
vibrate hands-free
silent, divert to voicemail loud, vibrate

25. PhoneAdapter A-FSM
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast

26. PhoneAdapter A-FSM
ActivateMeeting DeactivateMeeting
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast

27. Example Faults in
PhoneAdapter
General Office
Home

28. Example Faults in
PhoneAdapter
General Office
Home
User’s phone discovers office PC at home (or vice versa)

29. Example Faults in
PhoneAdapter
General Office
Home
Nondeterminism!

30. Example Faults in
PhoneAdapter
General

31. Example Faults in
PhoneAdapter
Outdoor General
User leaves home

32. Example Faults in
PhoneAdapter
Outdoor General
Driving
User starts driving before Bluetooth detects hands-free system

33. Example Faults in
PhoneAdapter
Jogging
Outdoor General
Driving
Activation hazard!

34. Example Faults in
PhoneAdapter
Jogging
Outdoor General
Driving
Activation hazard!

35. Faults in CAAAs
• Behavioral Faults
Nondeterminism Unreachable state
Dead rule Activation race
Dead state Activation cycle

36. Faults in CAAAs
• Behavioral Faults
Nondeterminism Unreachable state
Dead rule Activation race
Dead state Activation cycle
• Hazards
Hold hazard Priority inversion
Activation hazard hazard

37. Why Not Use
Model Checkers?
- Difficult to encode fault patterns as
temporal logic formulae
✴ Bisimilar models may fail differently
- Difficult to encode rule logic as models in
common model checkers
✴ Predicates and actions label the transitions
- Difficult to interpret counterexamples as
faults in adaptation behavior

38. Algorithms
Basic Operation
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast
For each state
Analyze rules & inputs, and search along
transitions for instances of fault patterns

39. Algorithms
Basic Operation
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast
For each state
Analyze rules & inputs, and search along
transitions for instances of fault patterns

40. Algorithms
Basic Operation
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast
For each state
Analyze rules & inputs, and search along
transitions for instances of fault patterns

41. Algorithms
Basic Operation
Jogging Sync Meeting
Outdoor General Office
Driving
Home Driving
Fast
For each state
Analyze rules & inputs, and search along
transitions for instances of fault patterns

42. PhoneAdapter Results TABLE 2
BehavioralConfigurations Reported for PhoneAdapter
Faulty Input Faults: Enumerative, Symbolic
State Nondeterministic Dead Adaptation Unreachable
Adaptations Predicates Races Cycles States
General 37 1 45 13 0
Outdoor 3 0 135 23 0
Jogging 0 0 97 19 0
Driving 0 0 36 13 0
DrivingFast 0 0 58 19 0
Home 0 0 76 19 0
Office 0 0 29 1 0
Meeting 0 0 32 1 0
Sync 0 0 27 5 1

43. PhoneAdapter Results
Hazards: Enumerative
n PhoneAdapter Table 2: Faults
aptation Races and Cycles
State Vars. Context Hazards
Nondet. Adaptation Dead Pred
signments Race Cycle Paths Assignments
Hold Activ. Faults
Prior. Assignments
3968 General 13
45 14085
7 0 12811 3182
37 128
3968 135
Outdoor 23 161
5 0 32 0 352 17
3072 Jogging 19
97 22 0 4 0 00 1
2560 Driving 13
36 316 2 8 2 04 7
3072 DrivingFast
58 19 22 0 4 0 00 2
2816 Home
76 19 104
4 8 16 0 013 9
2848 O ce
29 1 82634
7 1828 128
368 2164
1 65
2048 Meeting 1
32 10 0 2 0 00 2
1024 Sync
27 5 22 2 4 0 00 1
ned a formal model of a key Contextbehavioral char-
6.4 Detecting complex Hazards
eristic, namely adaptation, of an increasingly sequences of asynchr
This class of faults corresponds to large and

44. Conclusion
Comparison of Approaches
Enumerative Symbolic Hybrid Planner
Local Search Local Search Local Search Global Search
Less Precise Less Precise Less Precise More Precise
Concrete Symbolic Symbolic Concrete
Counterexamples Counterexamples Counterexamples Counterexamples
Handles Smaller Handles Big Handles Bigger Sequential
State Spaces State Spaces State Spaces Search
Fast Faster Fastest Slowest

45. Future Work
Verification
• Continue the work on hazards and
planners
• Quantitative reasoning about faults
• Battery level, movement timings, etc.
• Online analysis of rules and faults

46. Future Work
Design
• Alternatives to rule-based adaptation!
• Machine learning approaches to context
classification and adaptation selection

47. Future Work
Design
• Alternatives to rule-based adaptation!
• Machine learning approaches to context
classification and adaptation selection
• Felicitous Computing Institute

48. Thank You!
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