Difference between pages "Code Generation Activity" and "D32 Model Animation"
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| − | + | = Model Animation = | |
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| − | ===== | + | == Overview == |
| − | + | === Siemens Application=== | |
| − | * | + | The most important additions of the last 12 months are: |
| − | * | + | * Application of ProB in three active deployments, namely the upgrading of the Paris Metro Line 1 for driverless trains, line 4 of the S\~{a}o Paulo metro and line 9 of the Barcelona metro. We also briefly report on experiments on the models of the CDGVAL shuttle. The paper |
| + | <ref name="fm2009">Michael Leuschel, Jérôme Falampin, Fabian Fritz and Daniel Plagge, Automated Property Verification for Large Scale B Models, FM'2009, LNCS 5850, Springer-Verlag, 2009</ref> | ||
| + | only contained the initial San Juan case study, which was used to evaluate the potential of our approach. | ||
| + | * In this article we describe the previous method adopted by Siemens in much more detail, as well as explaining the performance issues with Atelier B. | ||
| + | * Comparisons and empirical evaluations with other potential approaches and alternate tools (Brama, AnimB, BZ-TT and TLC) have been conducted. | ||
| + | * We provide more details about the ongoing validation process of ProB, which is required by Siemens for it to use ProB to replace the existing method. | ||
| + | The validation also lead to the discovery of errors in the English version of the Atelier B reference manual. | ||
| + | |||
| + | Also, since <ref name="fm2009"/>, ProB itself has been further improved inspired by the application, resulting in new optimisations in the kernel (see below). | ||
| − | + | More details: | |
| − | * | + | * <ref>Leuschel et al. FAC, special issue of FM'2009</ref> |
| − | + | * <ref>Leuschel et al. Draft of Validation Report</ref> | |
| − | * | ||
| − | |||
| − | === | + | === Multi-level Animation === |
| − | |||
| − | + | Prior versions of ProB only supported the animation of a single refinement level. Abstract variables and predicates referring to them were ignored. | |
| + | In | ||
| + | <ref name="abz2010">Stefan Hallerstede, Michael Leuschel and Daniel Plagge, Refinement-Animation for Event-B - Towards a Method of Validation, ASM'2010, LNCS 5977, Springer-Verlag, 2010</ref> | ||
| + | and | ||
| + | <ref name="ml-journal">Stefan Hallerstede, Michael Leuschel and Daniel Plagge, Validation of Formal Models by Refinement Animation, to appear in Science of Computer Programming, Elsevier</ref> | ||
| + | we extended ProB in a way that all refinement levels of a model can be animated simultaneously. | ||
| − | + | First, this can give the user a deeper insight into how the model behaves and how the refinement levels are related to each other. | |
| − | + | Second, we can now find errors in context of refinement. This include violation of the gluing invariant or not satisfiable witnesses for abstract variables. If such errors are present in a model, the corresponding proof obligation cannot be discharged. But without an animator it is not always easy to see for an user if this is caused by the complexity of the proof or by an error. | |
| − | + | In the articles we summarized Event-B's current refinement methodology and showed for each proof obligation how the algorithm would find a counter-example. We presented empirical results and discussed how the algorithm can be combined with symmetry reduction. | |
| − | + | === Evaluation of the ProB Constraint Solver === | |
| + | Various industrial applications have shown the need for improved constraint-solving capabilities (see CBC Deadlock, Test-Case Generation). In order to evaluate ProB, and detect areas for improvement, we have studied to what extent classical constraint satisfaction problems can be conveniently expressed as B predicates, and then solved by ProB. In particular, we have studied problems such as the n-Queens problem, graph colouring, graph isomorphism detection, time tabling, Sudoku, Hanoi, magic squares, Alphametic puzzles, and several more. We have then compared the performance with respect to other tools, such as the model checker TLC for TLA+, AnimB for Event-B, and Alloy. | ||
| + | |||
| + | The experiments show that some constraint satisfaction problems can be expressed very conveniently in B and solved very effectively with ProB. For example, TLC takes 8747 seconds (2 hours 25 minuts) to solve the 9-queens problem expressed as a logical predicate; Alloy 4.1.10 with minisat takes 0.406 seconds, ProB 1.3.3 takes 0.01 seconds. For 32 queens, ProB 1.3.3 takes 0.28 seconds, while Alloy 4.1.10 with minisat takes over 4 minutes (TLC was only able to solve the n-queens problem up until n=9, or n=14 when reformulating the problem as a model checking problem rather than a constraint-solving problem). In another small experiment, we checked whether two graphs with 9 nodes of out-degree exactly one are isomorphic by checking for the existence of a permutation which preserved the graph structure. TLC finds a permutation after 2 hours 6 minutes and 28 seconds; ProB 1.3.3 takes 0.01 seconds to find the same solution, while Alloy takes 0.11 seconds with SAT4J and 0.05 seconds with minisat. | ||
| + | For some other examples (in particular time-tabling) involving operators such as the relational image, the performance of ProB is still sub-optimal with respect to, e.g., Alloy; we plan to overcome this shortcoming in the future. Our long term goal is that B can not only be used to as a formal method for developing safety critical software, but also as a high-level constraint programming language. | ||
| − | === | + | === Constraint-Based Deadlock Checking === |
| + | Ensuring the absence of deadlocks is important for certain applications, in particular for Bosch's Adaptive Cruise Control. We are tackling the problem of finding deadlocks via constraint solving rather than by model checking. Indeed, model checking is problematic when the out-degree is very large. In particular, quite often there can be a practically infinite number of ways to instantiate the constants of a B model. In this case, model checking will only find deadlocks for the given constants | ||
| + | chosen. | ||
| − | + | The basic idea is to generate deadlocks by solving a constraint consisting of the axioms Ax, the invariants Inv together with a constraint D specifying a deadlock. More formally, D is the negation of the disjunction of all the guards. | |
| − | The | + | The following tool developments were required to meet the challenges raised by the industrial application: |
| + | * generation of the deadlock freedom proof obligation by ProB (to avoid dependence on other plug-ins and being able to control whether theorems are to be used or not; currently they are not used) | ||
| + | * implementation of a constraint-based deadlock checking algorithm: | ||
| + | ** with the possibility to specify an additional goal predicate to restrict the deadlock search to certain scenarios: in Bosch's case due to the flow plugin, one wants to restrict deadlock checking e.g. to states with the variable Counter set to 10 | ||
| + | ** with semantic relevance filtering (to be able to filter out guards which are always false given the goal predicate). | ||
| + | ** with partitioning of the constraint predicate into components and optionally reordering according to usage (basic predicates which occur in most guards are listed first) | ||
| + | * Improvements to ProB's constraint solving engine: (reification of constraints, detection of common sub-predicates, more precise information propagation for membership constraints, performance improvments in the typchecker and other parts of the kernel). | ||
| − | + | ProB has been applied successfully to two models of the adaptive cruise control by Bosch. The more complicated model is CrCtrl_Comb2Final. To give an idea, here are some statistics of the deadlock freedom proof obligation for CrCtrl_Comb2Final: | |
| + | * when printed in 9-point Courier ASCII the formula takes 32 A4 pages (the disjunction of the guards starts at page 6) | ||
| + | * the model contains 59 events with 837 guards (19 of them disjunctions, some of which themselves nested) | ||
| + | * Bosch are interested in deadlocks that are possible according to a flow specified using the flow plugin; these can be found with ProB by specifying a goal predicate (such as "Counter=10") | ||
| + | * the proof obligation (as generated by the flow plugin) initially could not be loaded in Rodin due to "Java Heap Space Error". | ||
| + | * Counter examples are found by ProB for various versions of the model in 9-24 seconds (including loading, typechecking and deadlock PO generation; the constraint solving time is 1.03 to 12.86 seconds). | ||
| − | + | === BMotionStudio for Industrial Models === | |
| − | + | Previously, we presented BMotion Studio, a visual editor which enables the developer of a formal model to set-up easily a domain specific visualization for discussing it with the domain expert. However, BMotion Studio had not yet reached the status of an Industrial strength tool due to the lack of important features known from modern editors. | |
| − | + | In this work we present the improvements to BMotion Studio mainly aimed at upgrading it to an industrial strength tool and to show that we can apply the benefits of BMotion Studio for visualizing more complex models which are on the level of industrial applications. In order to reach this level the contribution of this work consists of three parts: | |
| − | + | * We added a lot of new features to the graphical editor known from modern editors like: Copy-paste support, undo-redo support, rulers, guides and error reporting. One step towards was the redesign of the graphical editor with GEF. | |
| + | * Since extensibility is a very important design principle for reaching the level of an industrial strength tool we pointed up the extensibility options of BMotion Studio. | ||
| + | * We introduced the visualization for two models which are on the level of industrial applications in order to demonstrate that we can apply the benefits of BMotion Studio for visualizing more complex models. The first model is a mechanical press controller and the second model is a train system which manages the crossing of trains in a certain track network. | ||
| − | + | === Various other improvements === | |
| − | + | mainly inspired by Siemens and Bosch Applications | |
| − | + | * Improved AVL algorithms, more operators | |
| − | |||
| − | * | ||
| − | |||
| − | * | + | * record support: automatic detection of records described by a bijection between a cartesian product and a carrier set (these axioms can either be entered manually, such as in the Bosch models, or generated by the Records plug-in). |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | * | + | * treatment of infinite sets, in particular complement sets such as INTEGER \ {x}. Such sets are being used in some of the Siemens models. |
| − | |||
| − | |||
| − | |||
| − | + | * Partitioning of predicates into connected sub-components (was useful for Siemens application, to be able to pinpoint location of an inconsistency in the axioms; it turned out useful for constraint-based deadlock checking as well) | |
| − | * | + | * Improved constraint solving, better use of Prolog's CLP(FD) constraint solver |
| − | |||
| − | |||
| − | + | * reification of constraints, detection of common sub-predicates, more precise information propagation for membership constraints, performance improvments in the typchecker and other parts of the kernel | |
| − | * | ||
| − | |||
| − | |||
| − | == | + | == Motivations == |
| − | |||
| − | |||
| − | |||
| − | |||
| − | + | The above works were motivated mainly to support the following three industrial deployments: | |
| − | * | + | * Siemens: enable Siemens to use ProB in their SIL4 development chain, replacing Atelier B for data validation. |
| + | * Bosch: provide animation and constraint-based deadlock detection for the Adaptive Cruise Control | ||
| + | * SAP: provide a way to generate test cases using constraint-based animation | ||
| − | == | + | == Available Documentation == |
| − | |||
| + | === References === | ||
| + | <references/> | ||
| − | + | == Planning == | |
| − | + | * Finish Validation Report | |
| − | + | * Write up Constraint-Based Deadlock Checking and integrate fully into Rodin Platform | |
| − | + | * Support mathematical extensions in ProB | |
| − | + | * Further improvements in the constraint-solving kernel of ProB; in particular for relations and operators. A Kodkod translator is being developed. | |
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Revision as of 08:01, 30 November 2010
Model Animation
Overview
Siemens Application
The most important additions of the last 12 months are:
- Application of ProB in three active deployments, namely the upgrading of the Paris Metro Line 1 for driverless trains, line 4 of the S\~{a}o Paulo metro and line 9 of the Barcelona metro. We also briefly report on experiments on the models of the CDGVAL shuttle. The paper
[1] only contained the initial San Juan case study, which was used to evaluate the potential of our approach.
- In this article we describe the previous method adopted by Siemens in much more detail, as well as explaining the performance issues with Atelier B.
- Comparisons and empirical evaluations with other potential approaches and alternate tools (Brama, AnimB, BZ-TT and TLC) have been conducted.
- We provide more details about the ongoing validation process of ProB, which is required by Siemens for it to use ProB to replace the existing method.
The validation also lead to the discovery of errors in the English version of the Atelier B reference manual.
Also, since [1], ProB itself has been further improved inspired by the application, resulting in new optimisations in the kernel (see below).
More details:
Multi-level Animation
Prior versions of ProB only supported the animation of a single refinement level. Abstract variables and predicates referring to them were ignored. In [4] and [5] we extended ProB in a way that all refinement levels of a model can be animated simultaneously.
First, this can give the user a deeper insight into how the model behaves and how the refinement levels are related to each other.
Second, we can now find errors in context of refinement. This include violation of the gluing invariant or not satisfiable witnesses for abstract variables. If such errors are present in a model, the corresponding proof obligation cannot be discharged. But without an animator it is not always easy to see for an user if this is caused by the complexity of the proof or by an error.
In the articles we summarized Event-B's current refinement methodology and showed for each proof obligation how the algorithm would find a counter-example. We presented empirical results and discussed how the algorithm can be combined with symmetry reduction.
Evaluation of the ProB Constraint Solver
Various industrial applications have shown the need for improved constraint-solving capabilities (see CBC Deadlock, Test-Case Generation). In order to evaluate ProB, and detect areas for improvement, we have studied to what extent classical constraint satisfaction problems can be conveniently expressed as B predicates, and then solved by ProB. In particular, we have studied problems such as the n-Queens problem, graph colouring, graph isomorphism detection, time tabling, Sudoku, Hanoi, magic squares, Alphametic puzzles, and several more. We have then compared the performance with respect to other tools, such as the model checker TLC for TLA+, AnimB for Event-B, and Alloy.
The experiments show that some constraint satisfaction problems can be expressed very conveniently in B and solved very effectively with ProB. For example, TLC takes 8747 seconds (2 hours 25 minuts) to solve the 9-queens problem expressed as a logical predicate; Alloy 4.1.10 with minisat takes 0.406 seconds, ProB 1.3.3 takes 0.01 seconds. For 32 queens, ProB 1.3.3 takes 0.28 seconds, while Alloy 4.1.10 with minisat takes over 4 minutes (TLC was only able to solve the n-queens problem up until n=9, or n=14 when reformulating the problem as a model checking problem rather than a constraint-solving problem). In another small experiment, we checked whether two graphs with 9 nodes of out-degree exactly one are isomorphic by checking for the existence of a permutation which preserved the graph structure. TLC finds a permutation after 2 hours 6 minutes and 28 seconds; ProB 1.3.3 takes 0.01 seconds to find the same solution, while Alloy takes 0.11 seconds with SAT4J and 0.05 seconds with minisat. For some other examples (in particular time-tabling) involving operators such as the relational image, the performance of ProB is still sub-optimal with respect to, e.g., Alloy; we plan to overcome this shortcoming in the future. Our long term goal is that B can not only be used to as a formal method for developing safety critical software, but also as a high-level constraint programming language.
Constraint-Based Deadlock Checking
Ensuring the absence of deadlocks is important for certain applications, in particular for Bosch's Adaptive Cruise Control. We are tackling the problem of finding deadlocks via constraint solving rather than by model checking. Indeed, model checking is problematic when the out-degree is very large. In particular, quite often there can be a practically infinite number of ways to instantiate the constants of a B model. In this case, model checking will only find deadlocks for the given constants chosen.
The basic idea is to generate deadlocks by solving a constraint consisting of the axioms Ax, the invariants Inv together with a constraint D specifying a deadlock. More formally, D is the negation of the disjunction of all the guards.
The following tool developments were required to meet the challenges raised by the industrial application:
- generation of the deadlock freedom proof obligation by ProB (to avoid dependence on other plug-ins and being able to control whether theorems are to be used or not; currently they are not used)
- implementation of a constraint-based deadlock checking algorithm:
- with the possibility to specify an additional goal predicate to restrict the deadlock search to certain scenarios: in Bosch's case due to the flow plugin, one wants to restrict deadlock checking e.g. to states with the variable Counter set to 10
- with semantic relevance filtering (to be able to filter out guards which are always false given the goal predicate).
- with partitioning of the constraint predicate into components and optionally reordering according to usage (basic predicates which occur in most guards are listed first)
- Improvements to ProB's constraint solving engine: (reification of constraints, detection of common sub-predicates, more precise information propagation for membership constraints, performance improvments in the typchecker and other parts of the kernel).
ProB has been applied successfully to two models of the adaptive cruise control by Bosch. The more complicated model is CrCtrl_Comb2Final. To give an idea, here are some statistics of the deadlock freedom proof obligation for CrCtrl_Comb2Final:
- when printed in 9-point Courier ASCII the formula takes 32 A4 pages (the disjunction of the guards starts at page 6)
- the model contains 59 events with 837 guards (19 of them disjunctions, some of which themselves nested)
- Bosch are interested in deadlocks that are possible according to a flow specified using the flow plugin; these can be found with ProB by specifying a goal predicate (such as "Counter=10")
- the proof obligation (as generated by the flow plugin) initially could not be loaded in Rodin due to "Java Heap Space Error".
- Counter examples are found by ProB for various versions of the model in 9-24 seconds (including loading, typechecking and deadlock PO generation; the constraint solving time is 1.03 to 12.86 seconds).
BMotionStudio for Industrial Models
Previously, we presented BMotion Studio, a visual editor which enables the developer of a formal model to set-up easily a domain specific visualization for discussing it with the domain expert. However, BMotion Studio had not yet reached the status of an Industrial strength tool due to the lack of important features known from modern editors.
In this work we present the improvements to BMotion Studio mainly aimed at upgrading it to an industrial strength tool and to show that we can apply the benefits of BMotion Studio for visualizing more complex models which are on the level of industrial applications. In order to reach this level the contribution of this work consists of three parts:
- We added a lot of new features to the graphical editor known from modern editors like: Copy-paste support, undo-redo support, rulers, guides and error reporting. One step towards was the redesign of the graphical editor with GEF.
- Since extensibility is a very important design principle for reaching the level of an industrial strength tool we pointed up the extensibility options of BMotion Studio.
- We introduced the visualization for two models which are on the level of industrial applications in order to demonstrate that we can apply the benefits of BMotion Studio for visualizing more complex models. The first model is a mechanical press controller and the second model is a train system which manages the crossing of trains in a certain track network.
Various other improvements
mainly inspired by Siemens and Bosch Applications
- Improved AVL algorithms, more operators
- record support: automatic detection of records described by a bijection between a cartesian product and a carrier set (these axioms can either be entered manually, such as in the Bosch models, or generated by the Records plug-in).
- treatment of infinite sets, in particular complement sets such as INTEGER \ {x}. Such sets are being used in some of the Siemens models.
- Partitioning of predicates into connected sub-components (was useful for Siemens application, to be able to pinpoint location of an inconsistency in the axioms; it turned out useful for constraint-based deadlock checking as well)
- Improved constraint solving, better use of Prolog's CLP(FD) constraint solver
- reification of constraints, detection of common sub-predicates, more precise information propagation for membership constraints, performance improvments in the typchecker and other parts of the kernel
Motivations
The above works were motivated mainly to support the following three industrial deployments:
- Siemens: enable Siemens to use ProB in their SIL4 development chain, replacing Atelier B for data validation.
- Bosch: provide animation and constraint-based deadlock detection for the Adaptive Cruise Control
- SAP: provide a way to generate test cases using constraint-based animation
Available Documentation
References
- ↑ 1.0 1.1 Michael Leuschel, Jérôme Falampin, Fabian Fritz and Daniel Plagge, Automated Property Verification for Large Scale B Models, FM'2009, LNCS 5850, Springer-Verlag, 2009
- ↑ Leuschel et al. FAC, special issue of FM'2009
- ↑ Leuschel et al. Draft of Validation Report
- ↑ Stefan Hallerstede, Michael Leuschel and Daniel Plagge, Refinement-Animation for Event-B - Towards a Method of Validation, ASM'2010, LNCS 5977, Springer-Verlag, 2010
- ↑ Stefan Hallerstede, Michael Leuschel and Daniel Plagge, Validation of Formal Models by Refinement Animation, to appear in Science of Computer Programming, Elsevier
Planning
- Finish Validation Report
- Write up Constraint-Based Deadlock Checking and integrate fully into Rodin Platform
- Support mathematical extensions in ProB
- Further improvements in the constraint-solving kernel of ProB; in particular for relations and operators. A Kodkod translator is being developed.
