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Inside LoLA Experiences from building a State Space Tool for Place Transition Nets Karsten Wolf Universität Rostock
1. Introduction
History ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties & Reduction Techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Stubborn sets Symmetries Coverability Sweep-Line Cycle coverage State compression Goal-oriented execution Distributed version Abstraction refinement
Plan ,[object Object],[object Object],[object Object]
Application: GALS wrapper ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hazard AND a b c 0 1 P(b) = 0 P(a) = 1 P(c) = 0 1 1 0 0 1 0 1 0  T 1 0 P(a): P(b): P(c): Hazard
The Wrapper
Size of wrapper ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Model: AND
Hazard is Marking! P(a) = 1 P(b) = 0 P(c) = 0 E(a)  E(b)
Verification ,[object Object],[object Object],[object Object],[object Object]
Reduction ,[object Object],[object Object],AND AND OR a b c d e a b c d e
LoLA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object]
Application: Validation of PN Semantics for BPEL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Process Sequence Flow A C D Switch B E Fault Handler Compensation Handler
Idea of the Petri net semantics ,[object Object],Sequence A E Flow ,[object Object],[object Object]
Example: receive (cont.)
Analysis results Stubborn sets, Sweep-line 6,300,000 10,000 states 440,000 1,300 red. states 1,069 249 transitions 410 158 places 53 17 activities Online Shop Purchase Order
Application: H. Garavel‘s challenge ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion, Part I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2. Implementation of core units
Core units ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1. Unfolding HL nets ,[object Object],[object Object],mailbox Proc x Proc tokens actually only for [x,y] with x N y Solutions: Maria: unfold HL net on-the-fly LoLA: propose redundant guards x N y x y mailbox
2. Firing transitions ,[object Object],[object Object],[object Object],[object Object],[object Object],For nets exhibiting locality, only „constant“ effort for firing
3. Checking state predicates ,[object Object],[object Object],  1  2  3  n ...   „ constant“ effort AND/OR true false
4. Managing the state space ,[object Object],[object Object],[object Object],... p 1   p 2  p 3  p 4  p 5   p 6   ... ... nr: 6
4 .Managing the state space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],... p 1   p 2  p 3  p 4  p 5   p 6   ... ... nr: 6
5. Organizing search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
5. Organizing search ,[object Object],[object Object],[object Object],[object Object]
6. Detecting strongly connected components ,[object Object],Tree edge Forward edge Backward edge Cross edge ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 3 2 6 1 5 4 0 1 1 3 4 4 4
6. Detecting strongly connected components ,[object Object],Tree edge Forward edge Backward edge Cross edge ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 3 2 6 1 5 4 0 1 1 1 1 4 4
Conclusion, Part II ,[object Object],[object Object],[object Object],[object Object]
Conclusion, Part II ,[object Object],[object Object],[object Object],[object Object],[object Object]
Reduction techniques
1. Linear algebra ,[object Object],[object Object],[object Object],Place invariant: token weights attached to places, weighted sum constant for all reachable markings Transition invariant: firing vector of a potential cycle
1. Linear algebra ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1. Linear algebra ,[object Object],[object Object],[object Object],[object Object],[object Object]
2. The sweep-line method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2. The sweep-line method ,[object Object],current state - + load persistent states from previous  sweep store states marked  persistent
3. The symmetry method ,[object Object],[object Object],[object Object],[object Object],[object Object]
3. The symmetry method ,[object Object],1 2 3 4 5 6 7 8 1st part: for every possibility to move node 1, insert one automorphism: (1 2 3 4) (5 6 7 8) (1 3) (2 4) (5 7) (6 8) (1 4 3 2) (5 8 7 6) (1 5 8 4) (2 6 7 3) (1 6) (2 5) (3 8) (4 7) (1 7) (2 8) (3 5) (4 6) (1 8) (2 7) (3 6) (4 5) 2nd part: fix 1, try to move node 2: (1) (2 4 5) (3 8 6) (7) (1) (2 5 4) (3 6 8) (7) 3rd part: fix 1,2, try to move node 3: (1) (2) (3 6) (4 5) (7) (8)
3. The symmetry method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
4. Stubborn set method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
4. The stubborn set method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Prod has more sophisticated, but slower techniques
Combination of techniques currentmarking := initial marking compute firing list do if firing list empty then  take care of scc check property backtrack else fire element of firing list search  &  insert if new check property compute firing list else backtrack Symmetries Stubborn sets Linear algebra
Conclusion Part III ,[object Object],[object Object],[object Object]
PTN versus CPN verification with significant knowledge of the model verification with little knowledge of the model Target group easier difficult Abstraction difficult to use easy to use Linear algebra manual or user-assisted automatic Additional information CPN PTN
General conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future of LoLA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
More information ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Inside LoLA - Experiences from building a state space tool for place transition nets

  • 1. Inside LoLA Experiences from building a State Space Tool for Place Transition Nets Karsten Wolf Universität Rostock
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  • 7. Hazard AND a b c 0 1 P(b) = 0 P(a) = 1 P(c) = 0 1 1 0 0 1 0 1 0  T 1 0 P(a): P(b): P(c): Hazard
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  • 11. Hazard is Marking! P(a) = 1 P(b) = 0 P(c) = 0 E(a) E(b)
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  • 17. Process Sequence Flow A C D Switch B E Fault Handler Compensation Handler
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  • 20. Analysis results Stubborn sets, Sweep-line 6,300,000 10,000 states 440,000 1,300 red. states 1,069 249 transitions 410 158 places 53 17 activities Online Shop Purchase Order
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  • 23. 2. Implementation of core units
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  • 47. Combination of techniques currentmarking := initial marking compute firing list do if firing list empty then take care of scc check property backtrack else fire element of firing list search & insert if new check property compute firing list else backtrack Symmetries Stubborn sets Linear algebra
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  • 49. PTN versus CPN verification with significant knowledge of the model verification with little knowledge of the model Target group easier difficult Abstraction difficult to use easy to use Linear algebra manual or user-assisted automatic Additional information CPN PTN
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Editor's Notes

  1. Pegelplätze: LL-Netz reicht aus
  2. P(c) ergibt sich aus P(a) und P(c) Wert des Events errechnet sich aus den Pegeln Im Modell wird von Zeit abstrahiert -> Events treten nebenläufig auf Können nichts über Gleichzeitigkeit sagen bzw. welcher Event vor welchem eintritt -> genau das Setting für hazards
  3. Markierungen oftmals schon nach kurzer Suche gefunden bzw. Markierung mittels sweepline und dann Markierung vergrössert und mittels stubborn sets Zeugenpfad
  4. What are interesting properties? -> deadlock, dead activities, will the customer always get an answer Other approaches are either not feature complete or have to much modelling power (ASMs) and they cannot be verified
  5. Model properties that are informally specified in the BPEL specification
  6. Remove the tokens