Correct-by-construction model driven engineering composition operators

Model composition is a crucial activity in Model Driven Engineering both to reuse validated and verified model elements and to handle separately the various aspects in a complex system and then weave them while preserving their properties. Many research activities target this compositional validation and verification (V & V) strategy: allow the independent assessment of components and minimize the residual V & V activities at assembly time. However, there is a continuous and increasing need for the definition of new composition operators that allow the reconciliation of existing models to build new systems according to various requirements. These ones are usually built from scratch and must be systematically verified to assess that they preserve the properties of the assembled elements. This verification is usually tedious but is mandatory to avoid verifying the composite system for each use of the operators. Our work addresses these issues, we first target the use of proof assistants for specifying and verifying compositional verification frameworks relying on formal verification techniques instead of testing and proofreading. Then, using a divide and conquer approach, we focus on the development of elementary composition operators that are easy to verify and can be used to further define complex composition operators. In our approach, proofs for the complex operators are then obtained by assembling the proofs of the basic operators. To illustrate our proposal, we use the Coq proof assistant to formalize the language-independent elementary composition operators Union and Substitution and the proof that the conformance of models with respect to metamodels is preserved during composition. We show that more sophisticated composition operators that share parts of the implementation and have several properties in common (especially: aspect oriented modeling composition approach, invasive software composition, and package merge) can then be built from the basic ones, and that the proof of conformance preservation can also be built from the proofs of basic operators.     

MUPPIT: a method for using proper patterns in model transformations

Software and Systems Modeling - Model transformation plays an important role in developing software systems using the model-driven engineering paradigm. Examples of applications of model...     

Control system synthesis using linear transformations

A new approach to the synthesis of linear multivariable control systems is presented. The main result demonstrates how a multivariable cross-coupled process within a closed loop may be transformed such that single-variable synthesis tools may be used to design the controller. Cross-coupled processes may be represented by a matrix equation. The new approach uses elementary transformations to diagonalize the polynomial matrix representing the open-loop process. This yieldsnuncoupled variables which describe the process. Closed-loop diagonalization is obtained by using a pro-multiplier matrix to relate the uncoupled variables to the physical variables of the process. This premultiplier matrix is a precontroller in the physical system. It allows synthesis to be carried out in the uncoupled coordinate system to get the physical coordinate system controller. The complete controller is designed using time or frequency single-variable synthesis techniques, and the uncoupled variables are handled one at a time during this phase. A second result follows from the main result. Disturbance inputs to the multivariable process are minimized just as they are in the single-variable case.     

BiLSTM regression model for face sketch synthesis using sequential patterns

Neural Computing and Applications - Face sketch synthesis from an input photo is a challenging task for law enforcement applications. Prior methods generally have required neighbor selection...     

A generic approach to detect design patterns in model transformations using a string-matching algorithm

Software and Systems Modeling - Maintaining software artifacts is a complex and time-consuming task. Like any other program, model transformations are subject to maintenance. In a maintenance...     

Towards the efficient development of model transformations using model weaving and matching transformations

Model transformations can be used in many different application scenarios, for instance, to provide interoperability between models of different size and complexity. As a consequence, they are becoming more and more complex. However, model transformations are typically developed manually. Several code patterns are implemented repetitively, thus increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper, we present a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Weaving models are models that contain different kinds of relationships between model elements. These relationships capture different transformation patterns. Matching transformations are a special kind of transformations that implement methods that create weaving models. We present a practical solution that enables the creation and the customization of different creation methods in an efficient way. We combine different methods, and present a metamodel-based method that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations. To validate our approach, we present an experiment using metamodels with distinct size and complexity, which show the feasibility and scalability of our solution.

Formal foundation of consistent EMF model transformations by algebraic graph transformation

Model transformation is one of the key activities in model-driven software development. An increasingly popular technology to define modeling languages is provided by the Eclipse Modeling Framework (EMF). Several EMF model transformation approaches have been developed, focusing on different transformation aspects. To validate model transformations with respect to functional behavior and correctness, a formal foundation is needed. In this paper, we define consistent EMF model transformations as a restricted class of typed graph transformations using node type inheritance. Containment constraints of EMF model transformations are translated to a special kind of graph transformation rules such that their application leads to consistent transformation results only. Thus, consistent EMF model transformations behave like algebraic graph transformations and the rich theory of algebraic graph transformation can be applied to these EMF model transformations to show functional behavior and correctness. Furthermore, we propose parallel graph transformation as a suitable framework for modeling EMF model transformations with multi-object structures. Rules extended by multi-object structures can specify a flexible number of recurring structures. The actual number of recurring structures is dependent on the application context of such a rule. We illustrate our approach by selected refactorings of simplified statechart models. Finally, we discuss the implementation of our concepts in a tool environment for EMF model transformations.     

Testing models and model transformations using classifying terms

This paper proposes the use of equivalence partitioning techniques for testing models and model transformations. In particular, we introduce the concept of classifying terms, which are general OCL terms on a class model enriched with OCL constraints. Classifying terms permit defining equivalence classes, in particular for partitioning the source and target model spaces of the transformation, defining for each class a set of equivalent models with regard to the transformation. Using these classes, a model validator tool is able to automatically construct object models for each class, which constitute relevant test cases for the transformation. We show how this approach of guiding the construction of test cases in an orderly, systematic and efficient manner can be effectively used in combination with Tracts for testing both directional and bidirectional model transformations and for analyzing their behavior.     

Reusable model transformations

Model transformations written for an input metamodel may often apply to other metamodels that share similar concepts. For example, a transformation written to refactor Java models can be applicable to refactoring UML class diagrams as both languages share concepts such as classes, methods, attributes, and inheritance. Deriving motivation from this example, we present an approach to make model transformations reusable such that they function correctly across several similar metamodels. Our approach relies on these principal steps: (1) We analyze a transformation to obtain an effective subset of used concepts. We prune the input metamodel of the transformation to obtain an effective input metamodel containing the effective subset. The effective input metamodel represents the true input domain of transformation. (2) We adapt a target input metamodel by weaving it with aspects such as properties derived from the effective input metamodel. This adaptation makes the target metamodel a subtype of the effective input metamodel. The subtype property ensures that the transformation can process models conforming to the target input metamodel without any change in the transformation itself. We validate our approach by adapting well known refactoring transformations (Encapsulate Field, Move Method, and Pull Up Method) written for an in-house domain-specific modeling language (DSML) to three different industry standard metamodels (Java, MOF, and UML).     

Change-driven model transformations

In this paper, we investigate change-driven model transformations, a novel class of transformations, which are directly triggered by complex model changes carried out by arbitrary transactions on the model (e.g. editing operation, transformation, etc). After a classification of relevant change scenarios, we identify challenges for change-driven transformations. As the main technical contribution of the current paper, we define an expressive, high-level language for specifying change-driven transformations as an extension of graph patterns and graph transformation rules. This language generalizes previous results on live model transformations by offering trigger events for arbitrarily complex model changes, and dedicated reactions for specific kinds of changes, making this way the concept of change to be a first-class citizen of the transformation language. We discuss how the underlying transformation engine needs to be adapted in order to use the same language uniformly for different change scenarios. The technicalities of our approach will be discussed on a (1) model synchronization case study with non-materialized target models and (2) a case study on detecting the violation of evolutionary (temporal) constraints in the security requirements engineering domain.     

Software model synthesis using satisfiability solvers

We introduce a novel approach for synthesis of software models based on identifying deterministic finite state automata. Our approach consists of three important contributions. First, we argue that in order to model software, one should focus mainly on observed executions (positive data), and use the randomly generated failures (negative data) only for testing consistency. We present a new greedy heuristic for this purpose, and show how to integrate it in the state-of-the-art evidence-driven state-merging (EDSM) algorithm. Second, we apply the enhanced EDSM algorithm to iteratively reduce the size of the problem. Yet during each iteration, the evidence is divided over states and hence the effectiveness of this algorithm is decreased. We propose—when EDSM becomes too weak—to tackle the reduced identification problem using satisfiability solvers. Third, in case the amount of positive data is small, we solve the identification problem several times by randomizing the greedy heuristic and combine the solutions using a voting scheme. The interaction between these contributions appeared crucial to solve hard software models synthesis benchmarks. Our implementation, called DFASAT, won the StaMinA competition.     

Scope in model transformations

A notion of hierarchical scope is commonplace in many programmatic systems. In the context of model, and in particular graph transformation, the use of scope can present two advantages: first, more natural expression of transformation application locality, and second, reduction in the number of match candidates, promising performance improvements. Previous work on scope, however, has focused on applying it to rule hierarchies, which reduces the number of matches performed, but not necessarily the cost of finding a single match. In this paper we define and explore a hierarchical scope formalism applied to the input graph, with associated modifications to the transformation rule definition. We then experimentally evaluate the benefits and challenges of our scoped model transformations in the state-of-the-art graph rewriting tool GrGen and our research-oriented, meta-modeling and rule-based model transformation tool AToMPM. We use a non-trivial “fire spreading” simulation transformation taken from distributed simulation community and a mutual exclusion transformation benchmark to demonstrate that integration of scope results in an elegant, intuitive, and efficient way of solving model transformation problems.     

Splitting-shooting methods for nonlinear transformations ofdigitized patterns

New splitting-shooting methods are presented for nonlinear transformations T: (ξ,η)→(x,y) where x=x(ξ,η), y=y(ξ,η). These transformations are important in computer vision, image processing, pattern recognition, and shape transformations in computer graphics. The methods can eliminate superfluous holes or blanks, leading to better images while requiring only modest computer storage and CPU time. The implementation of the proposed algorithms is simple and straightforward. Moreover, these methods can be extended to images with gray levels, to color images, and to three dimensions. They can also be implemented on parallel computers or VLSI circuits. A theoretical analysis proving the convergence of the algorithms and providing error bounds for the resulting images is presented. The complexity of the algorithms is linear. Graphical and numerical experiments are presented to verify the analytical results and to demonstrate the effectiveness of the methods     

Eye synthesis using the eye curve model

Eyes are a critical part in exhibiting facial expressions. Because of the appearance diversity of eyes due to motion, it is difficult to synthesize eye with a particular facial expression. Traditional methods have failed to adequately catch motion-related appearance changes. In order to generate a photorealistic expression eye, we propose a two-step method. First, we propose a curve-based model to represent eyes. The model uses one circle and four skewed elliptical arcs to represent the shape of eyes, and divides the entire eye region into six sub-regions that correspond to different anatomical components of eyes. Then we propose a structure-based-similarity (SBS) framework to synthesize expression eyes using the eye curve model. The main contributions of this paper are: first of all, the proposed eye curve model can represent the diversity of eyes due to motion and structure, which is better than some traditional models. Second, the SBS framework is flexible. In multiple samples and multiple targets situation, the framework can synthesize eyes which exhibit same expression with different personal style. In single sample and single target situation, the framework can clone this expression successful. Experimental results show that synthesized eyes are realistic and expressive.     

Propagation of constraints along model transformations using triple graph grammars and borrowed context

Fundamental properties of model transformations based on triple graph grammars (TGGs) have been studied extensively including syntactical correctness, completeness, termination and functional behavior. But up to now, it is an open problem how domain specific properties that are valid for a source model can be preserved along model transformations such that the transformed properties are valid for the derived target model. This question shows up in enterprise modeling. Here, modeling activities related to different domains are handled by different parties, and their models need to be consistent and integrated into one holistic enterprise model later on. So, support for decentralized modeling processes is needed. One technical aspect of the needed support in this case is the (bidirectional) propagation of constraints because that enables one party to understand and check the constraints of another party. Therefore, we analyze in the framework of TGGs how to propagate constraints from a source model to an integrated model and, afterwards, to a target model, such that, whenever the source model satisfies the source constraint, also the integrated and target model satisfy the corresponding integrated and target constraint. In our main new results we show under which conditions this is possible.     

Engineering model transformations with transML

Model transformation is one of the pillars of model-driven engineering (MDE). The increasing complexity of systems and modelling languages has dramatically raised the complexity and size of model transformations as well. Even though many transformation languages and tools have been proposed in the last few years, most of them are directed to the implementation phase of transformation development. In this way, even though transformations should be built using sound engineering principles—just like any other kind of software—there is currently a lack of cohesive support for the other phases of the transformation development, like requirements, analysis, design and testing. In this paper, we propose a unified family of languages to cover the life cycle of transformation development enabling the engineering of transformations. Moreover, following an MDE approach, we provide tools to partially automate the progressive refinement of models between the different phases and the generation of code for several transformation implementation languages.     

Managing loan customers using misclassification patterns of credit scoring model

A number of credit scoring models have been developed to evaluate credit risk of new loan applicants and existing loan customers, respectively. This study proposes a method to manage existing customers by using misclassification patterns of credit scoring model. We divide two groups of customers, the currently good and bad credit customers, into two subgroups, respectively, according to whether their credit status is misclassified or not by the neural network model. In addition, we infer the characteristics of each subgroup and propose management strategies corresponding to each subgroup.     

Matching patterns in strings subject to multi-linear transformations

Suppose we are given two strings of real numbers. The longer string is called text and the other is called pattern. We consider problems within the following framework. Suppose each symbol of the pattern was modified by any transformation which is a member in some family of transformations. Find all occurences of the pattern in the text where the pattern may appear subject to any one of these transformations. Problems are introduced and efficient algorithms are given.