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Searching for Liveness Property Violations in Concurrent Systems with ACO
- 1. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Searching for Liveness Property Violations in Concurrent Systems with ACO Francisco Chicano and Enrique Alba GECCO 2008, Atlanta, USA, July 12-16, 2008 1 / 34
- 2. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Motivation Motivation • Concurrent software is difficult to test ... • ... and it is in the heart of a lot of critical systems • Techniques for proving the correctness of concurrent software are required • Model checking → fully automatic • In the past the work using metaheuristics focused on safety properties • In this work we focus on liveness properties GECCO 2008, Atlanta, USA, July 12-16, 2008 2 / 34
- 3. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Explicit State Model Checking • Objective: Prove that model M satisfies the property : • HSF-SPIN: the property f is an LTL formula Model M LTL formula ¬ f Intersection Büchi automaton (never claim) s0 !p∨q s0 s5 s3 ∩ s0 s2 = s1 s3 s1 p∧!q q s2 s2 s5 s1 s7 s4 s4 s6 s8 s9 GECCO 2008, Atlanta, USA, July 12-16, 2008 3 / 34
- 4. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Explicit State Model Checking • Objective: Prove that model M satisfies the property : • HSF-SPIN: the property f is an LTL formula Model M LTL formula ¬ f Intersection Büchi automaton (never claim) s0 !p∨q s0 s5 s3 ∩ s0 s2 = s1 s3 s1 p∧!q q s2 s2 s5 s1 s7 s4 s4 s6 s8 s9 GECCO 2008, Atlanta, USA, July 12-16, 2008 4 / 34
- 5. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Explicit State Model Checking • Objective: Prove that model M satisfies the property : • HSF-SPIN: the property f is an LTL formula Model M LTL formula ¬ f Intersection Büchi automaton (never claim) s0 !p∨q s0 s5 s3 ∩ s0 s2 = s1 s3 s1 p∧!q q s2 s2 s5 s1 s7 s4 s4 s6 s8 s9 Using Nested-DFS GECCO 2008, Atlanta, USA, July 12-16, 2008 5 / 34
- 6. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs State Explosion Problem • Number of states very large even for small models s0 s1 s3 s2 Memory s5 s7 s4 s6 s8 s9 • Example: Dining philosophers with n philosophers → 3n states 20 philosophers → 1039 GB for storing the states • Solutions: collapse compression, minimized automaton representation, bitstate hashing, partial order reduction, symmetry reduction • Large models cannot be verified but errors can be found GECCO 2008, Atlanta, USA, July 12-16, 2008 6 / 34
- 7. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Heuristic Model Checking • The search for errors can be directed by using heuristic information 5 s0 2 2 Heuristic value s1 3 s3 s2 1 s5 0 s7 4 s4 0 s6 6 s8 7 s9 • Different kinds of heuristic functions have been proposed in the past: • Formula-based heuristics • Deadlock-detection heuristics • Structural heuristics • State-dependent heuristics GECCO 2008, Atlanta, USA, July 12-16, 2008 7 / 34
- 8. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Safety and Liveness Properties Safety property s0 s1 s3 s2 s5 s7 s4 • Counterexample ≡ path to accepting state s6 • Graph exploration algorithms can be used: DFS and BFS s8 s9 Liveness property s0 s1 s3 s2 s5 s7 • Counterexample ≡ path to accepting cycle s4 s6 • It is not possible to apply DFS or BFS s8 s9 GECCO 2008, Atlanta, USA, July 12-16, 2008 8 / 34
- 9. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Strongly Connected Components a b c e f g d GECCO 2008, Atlanta, USA, July 12-16, 2008 9 / 34
- 10. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Strongly Connected Components GECCO 2008, Atlanta, USA, July 12-16, 2008 10 / 34
- 11. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Strongly Connected Components GECCO 2008, Atlanta, USA, July 12-16, 2008 11 / 34
- 12. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Explicit State MC State Explosion Heuristic MC Safety & Liveness SCCs Strongly Connected Components N-SCC a b P-SCC c e F-SCC f g d GECCO 2008, Atlanta, USA, July 12-16, 2008 12 / 34
- 13. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live Optimization/Search Techniques OPTIMIZATION/SEARCH TECHNIQUES OPTIMIZATION/SEARCH TECHNIQUES EXACT EXACT APPROXIMATED APPROXIMATED Ad Hoc Heuristics Ad Hoc Heuristics METAHEURISTICS METAHEURISTICS Based on Calculus Based on Calculus Enumeratives Enumeratives Trayectory-based Trayectory-based Population-based Population-based • Newton • Depth First Search • SA • EA • Gradient • Branch and Bound • VNS • ACO • TS • PSO GECCO 2008, Atlanta, USA, July 12-16, 2008 13 / 34
- 14. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACO: Introduction • Ant Colony Optimization (ACO) metaheuristic is inspired by the foraging behaviour of real ants • ACO Pseudo-code GECCO 2008, Atlanta, USA, July 12-16, 2008 14 / 34
- 15. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACO: Construction Phase • The ant selects its next node stochastically • The probability of selecting one node Trail k depends on the pheromone trail and the τij heuristic value (optional) of the edge/node Heuristic i ηij Ni • The ant stops when a complete solution is built m j l k GECCO 2008, Atlanta, USA, July 12-16, 2008 15 / 34
- 16. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACO: Pheromone Update • Pheromone update During the construction phase with After the construction phase with • Trail limits (particular of MMAS) Pheromones are kept in the interval [τmin, τmax] GECCO 2008, Atlanta, USA, July 12-16, 2008 16 / 34
- 17. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACOhg: Huge Graphs Exploration The length of the ant paths is limited by λant What if…? λant Objective node Initial node Starting nodes for path construction change After σs steps Second stage Third stage GECCO 2008, Atlanta, USA, July 12-16, 2008 17 / 34
- 18. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACOhg-live • The search is an alternation of two phases First phase: search for accepting states Second phase: search for cycles from the accepting states ACOhg-live Pseudocode First phase GECCO 2008, Atlanta, USA, July 12-16, 2008 18 / 34
- 19. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACOhg-live • The search is an alternation of two phases First phase: search for accepting states Second phase: search for cycles from the accepting states ACOhg-live Pseudocode Second phase GECCO 2008, Atlanta, USA, July 12-16, 2008 19 / 34
- 20. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACOhg-live • The search is an alternation of two phases First phase: search for accepting states Second phase: search for cycles from the accepting states ACOhg-live Pseudocode Tabu list GECCO 2008, Atlanta, USA, July 12-16, 2008 20 / 34
- 21. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Metaheuristics ACO ACOhg ACOhg-live ACOhg-live • The search is an alternation of two phases First phase: search for accepting states Second phase: search for cycles from the accepting states ACOhg-live Pseudocode • Improvement Tabu list using SCCs First phase: accepting states in N-SCC are ignored Both phases: cycle detected in F-SCC is a violation GECCO 2008, Atlanta, USA, July 12-16, 2008 21 / 34
- 22. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Promela Models • We used 11 Promela models for the experiments Model LoC Scalable Processes LTL formula (liveness) alter 64 no 2 □(p → ◊q) ^ □(r → ◊s) giopj 740 yes j+6 □(p → ◊q) phij 57 yes j+1 □(p → ◊q) elevj 191 yes j+3 □(p → ◊q) sgc 1001 no 20 ◊p • Parameters for ACOhg-live Parameter msteps colsize λant σs ξ a ρ α β 1st phase 10 20 0.7 100 4 5 0.2 1.0 2.0 2nd phase 20 4 0.5 • Formula-based and finite state machine heuristics • ACOhg-live implemented in HSF-SPIN • 100 independent executions and statistical validation GECCO 2008, Atlanta, USA, July 12-16, 2008 22 / 34
- 23. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Promela Models • We used 11 Promela models for the experiments Model LoC Scalable Processes LTL formula (liveness) alter 64 no 2 □(p → ◊q) ^ □(r → ◊s) j=10,15,20 giopj 740 yes j+6 □(p → ◊q) phij 57 yes j=20,30,40 j+1 □(p → ◊q) elevj 191 yes j+3 □(p → ◊q) sgc 1001 j=10,15,20 no 20 ◊p • Parameters for ACOhg-live Parameter msteps colsize λant σs ξ a ρ α β 1st phase 10 20 0.7 100 4 5 0.2 1.0 2.0 2nd phase 20 4 0.5 • Formula-based and finite state machine heuristics • ACOhg-live implemented in HSF-SPIN • 100 independent executions and statistical validation GECCO 2008, Atlanta, USA, July 12-16, 2008 23 / 34
- 24. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results I: Influence of the SCC Improvement SD SD GECCO 2008, Atlanta, USA, July 12-16, 2008 24 / 34
- 25. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results I: Influence of the SCC Improvement SD SD SD SD SD SD SD SD GECCO 2008, Atlanta, USA, July 12-16, 2008 25 / 34
- 26. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results I: Influence of the SCC Improvement SD SD SD SD SD SD SD SD SD SD SD GECCO 2008, Atlanta, USA, July 12-16, 2008 26 / 34
- 27. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results I: Influence of the SCC Improvement SD 575192 SD SD SD SD SD SD SD SD SD SD SD SD SD SD SD SD SD GECCO 2008, Atlanta, USA, July 12-16, 2008 27 / 34
- 28. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results II: ACOhg-live+ vs. NDFS and INDFS Models ACOhg-live+ NDFS INDFS alter giop10 giop15 giop20 phi20 phi30 phi40 elev10 elev15 elev20 sgc GECCO 2008, Atlanta, USA, July 12-16, 2008 28 / 34
- 29. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results II: ACOhg-live+ vs. NDFS and INDFS Models ACOhg-live+ NDFS INDFS alter SD 1069 giop10 giop15 10001 1059 giop20 10001 phi20 9999 phi30 phi40 elev10 elev15 elev20 sgc GECCO 2008, Atlanta, USA, July 12-16, 2008 29 / 34
- 30. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results II: ACOhg-live+ vs. NDFS and INDFS Models ACOhg-live+ NDFS INDFS alter SD 1069 giop10 392192 giop15 388096 1059 15360 10001 giop20 10001 phi20 9999 phi30 phi40 elev10 elev15 elev20 sgc GECCO 2008, Atlanta, USA, July 12-16, 2008 30 / 34
- 31. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion Results II: ACOhg-live+ vs. NDFS and INDFS Models ACOhg-live+ NDFS INDFS alter SD 1069 giop10 392192 giop15 15320 388096 1059 15360 10001 15670 giop20 10001 phi20 9999 phi30 phi40 elev10 elev15 elev20 sgc GECCO 2008, Atlanta, USA, July 12-16, 2008 31 / 34
- 32. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Models & parameters Results Discussion How to use ACOhg-live • ACOhg-live should be used in the first/middle stages of the software development, when software errors are expected • ACOhg-live can also be used in other phases of the software development for testing concurrent software Large model or a short couterexample is needed ACOhg-live Model Small model and (I)NDFS any counterexample is needed fast GECCO 2008, Atlanta, USA, July 12-16, 2008 32 / 34
- 33. Algorithmic Conclusions Introduction Background Experiments Proposal & Future Work Conclusions & Future Work Conclusions & Future Work Conclusions • ACOhg-live is the first algorithm based on metaheuristics (to the best of our knowledge) applied to the search for liveness errors in concurrent models • The improvement based on the SCCs of the never claim outperforms the efficacy of ACOhg-live • ACOhg-live is able to outperform (Improved) Nested-DFS in efficacy and efficiency in the search for liveness errors Future Work • Analysis of parameterization for reducing the parameters • Include ACOhg-live into JavaPathFinder for finding liveness errors in Java programs • Combine ACOhg-live with techniques for reducing the memory required for the search such as partial order reduction (work in progress) GECCO 2008, Atlanta, USA, July 12-16, 2008 33 / 34
- 34. Searching for Liveness Property Violations in Concurrent Systems with ACO Thanks for your attention !!! GECCO 2008, Atlanta, USA, July 12-16, 2008 34 / 34