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Program Analysis Techniques for Model Queries and Transformations
The document summarizes three contributions to the analysis of model queries and transformations: C1) Lightweight static analysis techniques like type checking, type inference, and constraint graph analysis; C2) Defining and maintaining incremental graphical views of models using annotated graph queries; C3) Slicing techniques for model queries and transformations to help debug programs. The techniques were implemented in the VIATRA model transformation framework and influenced related tools. Evaluations showed the techniques scale to large models and complex transformations.
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Program Analysis Techniques for Model Queries and Transformations
- 1. Budapest University of Technology and Economics Department of Measurement and Information Systems Program Analysis Techniques for ModelQueries and Transformations Zoltán Ujhelyi Advisor: Dr. Dániel Varró
- 2. Model-driven Engineering § Modeling tools are ubiquitous in systems engineering o SW/HW Architecture oPlatform description § Common tasks in tools o Model validation o Artifact generation o Graphical views § Inside these tools o Models as graphs o Constraints as graph patterns o Model updates as graph transformations
- 3. Model Queries and Transformations Graph Patterns • Conditions and constraints expressed as graphs • Different pattern matching strategies •Local search • Incremental evaluation (e.g. Rete networks) Graph Transformation Rules • Elementary transformation steps • LHS pattern as condition • RHS pattern describes action Complex Transformations • Ordered rule applications • Rule calls and dependencies • Often formulated using imperative constructs pl : Place LHS - precondition RHS - action to : Token X: tokens pl : Place to : Token X: tokens pl : Place
- 4. Analysis of Model Queries and Transformations § Existing: verification and validation approaches o E.g. testing, theorem proving or model checking oHigh level of effort required to use § Goal: lightweight analysis techniques o To assist developers of queries and transformations Transformation Programs Incremental Graphical Views Lightweight Static Analysis Graph Pattern Graph Transformation Complex Model Transformation Slicing
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- 6. Transformation Language Assumptions • Misspellings • Parameter mismatches Textual language •Declarative: graph patterns and graph transformation rules • Imperative: complex control structure Imperative and declarative parts • Multiple inheritance • Dynamic typing Complex type system
- 7. Type Checking and Inference § Problem: Mistyping variables results in subtle issues o Empty result set for model queries §Solution o Type rules: collect type judgements from language elements o Type safety as constraint satisfaction problems (CSP) o Back-annotation of results to program pattern placeWithToken(pl : Place, to : Token) { Place.tokens(to, pl); } TS ⊢ pl : Place TS ⊢ to : Token Type judgements TS ⊢ to : Place ∧pl : Token CSP
- 8. Constraint Graph Analysis – 1. § Connectedness analysis oIdentifies independent subpatterns o Expensive match set calculation (Cartesian product) o Suggests missing constraint pattern sourcePlaceUnconnected (p1 : Place, p2: Place) { Transition(t1); Transition(t2); find sourcePlace(t1, p1); find targetPlace(t2, p2); // Missing: t1 == t2; } p1 p2 t1 t2 Ccall sourcePlace Ccall targetPlace Param #2 Param #2 Param #1 Param #1 Cent Place Cent Place entity entity
- 9. Constraint Graph Analysis – 2. § Variable usage counting oIdentify unused variables (referenced only once) o Suggest misspelt variable name pattern placeWithTokenUnused (pl : Place) { //pl and p1 are different Place.tokens(p1, _); } pl p1_ Crel Place.tokens Param #2 Param #1 Cent Place entity
- 10. C1: Lightweight Static Analysis Techniques C1.1. Type checking and typeinference with constraint satisfaction problems C1.2. Type judgements of model queries C1.3. Type judgements of transformation rules C1.4. Variable usage counting and connectedness analysis for graph patterns Collaboration: Gábor Bergmann
- 11. Evaluation and Exploitation § Evaluation o Type checking •Good performance for declarative parts • Slowdown in imperative control structures o Usage counting and connectedness analysis • Positive feedback from external VIATRA users § Exploitation of results: o Available in current release of VIATRA o Influenced the pattern language of EMF-IncQuery • More compact syntax assuming type inference • Live type checking while typing Program LoC Time (s) Petri net simulator 120 0.1 / 0.1 Petri net generator 94 1.2 / 0.9 Antworld 300 0.2 / 0.3 BPEL2SAL 8339 - / 69
- 12. Publications Related to Contribution 1 Journal paper • Z. Ujhelyi, Á. Hegedüs, G. Bergmann, Á. Horváth, I. Ráth and D. Varró. ‘EMF- IncQery: An Integrated Development Environment for Live Model Queries’. In: Science of Computer Programming 98, Part 1 (2015). •Z. Ujhelyi, Á. Horváth, and D. Varró. “Static Type Checking of Model Transformation Programs”. In: ECEASST 38 (2010). International conferences • G. Bergmann, Z. Ujhelyi, I. Ráth, and D. Varró. “A Graph Query Language for EMF Models”. In: Theory and Practice of Model Transformations. LNCS 6707.
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- 14. Challenges for Graphical View Models • Capture an aspect of an underlying model • Displayed in a human understandable notation Abstract views of models •A subset of the model is often related to the current issue • Requires easy definition of views Views for debugging • How to define the required abstraction? • How to update the view models on model changes? • Different view models require different rendering View development is expensive
- 15. Defining View Models Model RenderersRenderersView Notation Transformation Notation model Graph patterns Graph patterns Graph patterns &'()'(" &*+('" ,-'(." /'01+23" 456'2*" Any model can be reused Multiple renderers available, e.g. list, tree or graph Simple notation model with base elements, such as items, edges or containment relations @Item(item =pl, label = "$pl.name$") pattern place(pl : Place) { … } @Containment(container = pl, item = to) pattern tokens(pl : Place, to : Token) { … } Transformation defined by annotated graph patterns
- 16. C2: Incremental Graphical Views C2.1. Graphical view definition usingannotated queries C2.2. Workflow for incremental graphical view maintenance Collaboration: Csaba Debreceni, Péter Lunk
- 17. Evaluation and Exploitation § Scalability of view calculation (by C. Debreceni) o Models up to 100k model elements oInitialization time acceptable o Update time instantaneous § Exploitation o VIATRA Debugger o Model Visualization in Trans-IMA project
- 18. Publications related to Contribution 2 Journal paper • Z. Ujhelyi, Á. Hegedüs, G. Bergmann, Á. Horváth, I. Ráth and D. Varró. “EMF- IncQuery: An Integrated Development Environment for Live Model Queries”. In: Science of Computer Programming 98, Part 1 (2015). International conferences •C. Debreceni, Á. Horváth, Á. Hegedüs, Z. Ujhelyi, I. Ráth, and D. Varró. “Query- driven Incremental Synchronization of View Models”. In: Proceedings of the 2nd Workshop on View-Based, Aspect-Oriented and Orthographic Software Modelling.
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- 20. Challenge of Debugging Model Transformations • Why has a certain match appeared in the results? •Why are some matches missing from the results? Debugging questions related to model queries • Why is a certain statement executed? • Why is a certain model element created? Debugging questions related to transformations • Hidden data dependencies • Declarative rules • Control flow dependencies Identifying dependencies Similar to program slicing problem
- 21. Slicing Model Transformations § As in program slicing o Systematic collection of dependencies oBUT: slicing of (input) models is also required § Two approaches o Transformation slicing based on execution traces o Query slicing based on Rete networks Model transformation slicing Query / Transformation Models Transformation Slice Model Slice Criterion
- 22. Slicing Model Transformations – An Example rule fireTransition(inT) = { if (find isFireable(T)) { // remove tokens from source places forall Pl1 with find sourcePlace(T,Pl1) do apply removeToken(Pl1); // creates tokens on target places forall Pl2 with find targetPlace(T,Pl2) do apply addToken(Pl2); } } Client Server Query Respond Store
- 23. C3: Slicing Model Queries and Transformations C3.1. Slicing of Model Transformations C3.2. DynamicBackward Slicing of Model Queries C3.3. Dynamic Backward Slicing of Model Transformation Programs C3.4. Evaluation of the Dynamic Backward Slicing Approaches
- 24. Evaluation § Case studies § Results oSlices reflect the characteristics of the transformation o Approach scales both with • Model size and transformation complexity § Exploitation o Rete visualizer and debugger architecture in VIATRA • Baseline of future slicing support Petri net simulation • Simple transformation, large models Antworld Case Study • Complex simulation • Ever-growing models BPEL2SAL transformation • Complex imperative control structure Reengineering Case Study • Model Extraction
- 25. Publications Related to Contribution 3 International conferences • Z. Ujhelyi, G. Bergmann,and D. Varró. “Rete network slicing for model queries”. In: Graph Transformation. LNCS 9761. 2016 •Z. Ujhelyi, Á. Horváth, and D. Varró. “Dynamic Backward Slicing of Model Transformations”. In: Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verication and Validation. ICST ’12. IEEE Computer Society, 2012 • Z. Ujhelyi, Á. Horváth, and D. Varró. “Towards Dynamic Backwards Slicing of Model Transformations”. In: Automated Software Engineering, 26th IEEE/ACM Int. Conf. on. 2011, pp. 404–407
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- 28. Publication Overview § Number of publications: 26 o International journal articles: 5 oInternational conference papers: 9 o International workshop papers: 6 o National conference papers: 4 o Technical reports: 2 § Independent citations (MTMT): 59 § IEEE Best Paper award in 2014 o Z. Ujhelyi, Á. Horváth, D. Varró, N. I. Csiszár, G. Szőke, L. Vidács and R. Ferenc. ‘Anti-pattern detection with model queries: A comparison of approaches’. In: Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE)
- 29. § VIATRA model transformation framework o Type checking for the transformation language oIncremental Graphical Viewers implementation § EMF-INCQUERY model query framework o Static type checking in pattern language o Constraint graph analysis included in pattern language § Known users of the framework include Applications of Results in Modeling Tools Co-lead since 2013 Project merged into VIATRA in 2016
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