1. Logic Programming Based Model Transformations An overview of related work

2. GOAL – WHY? Exploring the reasoning techniques for querying the inconsistencies in Models and model transformations State of the art, challenges, gaps and how we can contribute Simplify transformations between ISO and CCOM

3. Logic programming language RELATED WORK Answer Set Programming (ASP)  Declarative  Search problems to find stable models  answer set solvers — programs for generating stable models—are used to perform search Antonio Cicchetti, Davide Ruscio, Romina Eramo, and Alfonso Pierantonio. JTL: A Bidirectional and Change Propagating Transformation Language. In SLE 2011, volume LNCS 6563, pages 183-202. Springer, 2011. Johannes Schoenboeck, Angelika Kusel, Juergen Etzlstorfer, Elisabeth Kapsammer, Wieland Schwinger, Manuel Wimmer, and Martin Wischenbart. CARE - A Constraint-Based Approach for Re-Establishing Conformance Relationships. In APCCM 2014, CRPIT Vol.154, pages 19-28. ACS, 2014 Alloy  lightweight formal specification language  declarative based on first-order logic  based on relational logic > effective to validate and verify object-oriented models  automatic model finding via SAT solving  Alloy model consists of Signatures, Relations, Facts and Predicates.  Alloy Analyzer (Simulation & Assertion) Anastasakis, K., Bordbar, B., & Küster, J. M. (2007, October). Analysis of model transformations via Alloy. In Proceedings of the 4th MoDeVVa workshop Model-Driven Engineering, Verification and Validation (pp. 47-56). Nuno Macedo and Alcino Cunha. Implementing QVT-R bidirectional model transformations using Alloy. In Fundamental Approaches to Software Engineering, pages 297-311. Springer, 2013. Prolog  first-order logic  relations represents facts and rules. A computation is initiated by running a query over these relations Zoltan Balogh and Daniel Varro. Model transformation by example using inductive logic programming. SoSyM, 8(3):347-364, 2009. JesusM. Almendros-Jimenez and Luis Iribarne. A model transformation language based on logic programming. In SOFSEM 2013, volume LNCS 7741, pages 382-394. Springer, 2013.

5. CARE approach(ASP) Each metamodel elements > class, attribute and reference ASP facts. Model elements > object, value and link ASP facts. Conformance constrains have been manually defined [in pseudocode in table] and encoded in ASP. Meta-model specific Ecore/OCL constrains > ASP constrains in future version of prototype. Language semantics(object-oriented) need to be encoded as well. Ecore <> ASP transformations are implemented by Xtend (a flexible and expressive dialect of Java) A constraint solver produces an output, which is a set of facts describing which objects, values, and links are conform or not, and adds these new facts to the CARE knowledge base.

6. JTL approach(ASP) Modeled as Graphs composed of nodes, edges and properties – logic assertions (1)Each (meta)model elements > metanode, metaedge, and metaprop predicate symbols (1)Each model element > node, edge, property (2)JTL(QVT-R like syntax) > ASP relation and constrains by means of ATL transformation (4)Transformation (JTL) engine is based on ASP bidirectional transformation program executed by DLV solver(find stable models) Transformation process steps: 1. the execution engine induces all the possible solution candidates according to the specified relations; 2. the set of candidates is refined by means of constraints (ASP rules). (3)Target model is deducted based on ASP rules from candidate solutions.

7. Analysis of MT(Alloy) based on meta-modelling MOF meta-models/OCL > Alloy by means of UML2Alloy transformation tool Transformation rules > Alloy mapping relation STEPS Step 1:Translate the Model Transformation Specification to Alloy. Step 2: Analysis using the Alloy Analyzer. Alloy model of MT which can be analysed by Analyzer(Simulation)

8. QVT-R Bidirectional (Alloy) UML(annotated with OCL) + QVT-R > Alloy principle of least change restores consistency by simply returning target models that are at a minimal distance from the original -> the“check- before-enforce” policy (QVT-R) is satisfied. Classes and associations (including attributes) > signatures and relations in Alloy. Inheritance relationship in Alloy. For each relation R > Alloy predicates to specify R forward and R backward Predicates are used to specify the when and where clauses, and the domain patterns of each relation checkonly mode - check the consistency predicate for a pair of models

9. PTL approach(Prolog) Each (meta) model elements > set of classes, attributes, roles and helpers. Each model elements > set of classes, attributes, roles, helpers and objects. PTL > Prolog encoding - based on a Prolog library for handling meta- models PTL (ATL like syntax) = ATL style rules + logic Rules in Prolog(helpers). Prolog style rules serve as a query language. Prolog is used as a transformation engine for PTL. A Prolog program is automatically obtained from a PTL program. The encoding of PTL programs with Prolog is based on a Prolog library for handling meta-models

10. Inductive logic(Prolog) Modeled as Graphs composed of nodes, edges and properties – logic assertions Source and target models are mapped into Prolog clauses Mapping models are represented by tuples ref (ri, src1,..., srcn, trg1,..., trgm).

11. Conclusions Logic clause, fact, assertion = represents models and meta-models Logic rule = used to encode model transformation and constraints on models taking advantage of model finding(SAT-solver) abilities Being solver-based, the main drawback is performance - incremental solving techniques - mechanisms to infer which parts of target model can be fixed in design time Model & transformation > logic programming is manual, except in JTL approach