A Language-Independent and Formal Approach to Pattern-Based Modelling with Support for Composition and Analysis

Context: Patterns are used in different disciplines as a way to record expert knowledge for problem solving in specific areas. Their systematic use in Software Engineering promotes quality, standardization, reusability and maintainability of software artefacts. The full realisation of their power is however hindered by the lack of a standard formalization of the notion of pattern. Objective: Our goal is to provide a language-independent formalization of the notion of pattern, so that it allows its application to different modelling languages and tools, as well as generic methods to enable pattern discovery, instantiation, composition, and conflict analysis. Method: For this purpose, we present a new visual and formal, language-independent approach to the specification of patterns. The approach is formulated in a general way, based on graphs and category theory, and allows the specification of patterns in terms of (nested) variable submodels, constraints on their allowed variance, and inter-pattern synchronization across several diagrams (e.g. class and sequence diagrams for UML design patterns). Results: We provide a formal notion of pattern satisfaction by models and propose mechanisms to suggest model transformations so that models become consistent with the patterns. We define methods for pattern composition, and conflict analysis. We illustrate our proposal on UML design patterns, and discuss its generality and applicability on different types of patterns, e.g. workflow patterns, enterprise integration patterns and interaction patterns. Conclusion: The approach has proven to be powerful enough to formalize patterns from different domains, providing methods to analyse conflicts and dependencies that usually are expressed only in textual form. Its language independence makes it suitable for integration in meta-modelling tools and for use in Model-Driven Engineering.This work has been supported by the Visiting Professor Programmes of ‘‘Sapienza” University of Rome and its Department of Computer Science, the R&D program of the Community of Madrid (S2009/TIC-1650, project ‘‘e-Madrid”), the CAM-UC3M project ‘‘EXPLORE” (CCG08-UC3M/TIC-4487), as well as by the Spanish Ministry of Science and Innovation, under project ‘‘METEORIC” (TIN2008-02081), and mobility Grants JC2009-00015 and PR2009-0019.Publicad

Model Transformations in MT

Model transformations are recognised as a vital aspect of Model Driven Development,but current approaches cover only a small part of the possible spectrum. In this paper I present the MT model transformation which shows how a QVT-like language can be extended with novel pattern matching constructs, how tracing information can be automatically constructed and visualized, and how the transformed model is pruned of extraneous elements. As MT is implemented as a DSL within the Converge language, this paper also demonstrates how a general purpose language can be embedded in a model transformation language, and how DSL development can aid experimentation and exploration of new parts of the model transformation spectrum

Distribution pattern-driven development of service architectures

Distributed systems are being constructed by composing a number of discrete components. This practice is particularly prevalent within the Web service domain in the form of service process orchestration and choreography. Often, enterprise systems are built from many existing discrete applications such as legacy applications exposed using Web service interfaces. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed in a distributed environment. However, the amount of code required to realise these distribution patterns is considerable. In this paper, we propose a distribution pattern-driven approach to service composition and architecting. We develop, based on a catalog of patterns, a UML-compliant framework, which takes existing Web service interfaces as its input and generates executable Web service compositions based on a distribution pattern chosen by the software architect

Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.Comment: This paper is accepted the 24th International Conference On Neural Information Processing (ICONIP 2017). The previous submission to arXiv is replaced by this version because there was an error in Equation

Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

Generic Encodings of Constructor Rewriting Systems

Rewriting is a formalism widely used in computer science and mathematical logic. The classical formalism has been extended, in the context of functional languages, with an order over the rules and, in the context of rewrite based languages, with the negation over patterns. We propose in this paper a concise and clear algorithm computing the difference over patterns which can be used to define generic encodings of constructor term rewriting systems with negation and order into classical term rewriting systems. As a direct consequence, established methods used for term rewriting systems can be applied to analyze properties of the extended systems. The approach can also be seen as a generic compiler which targets any language providing basic pattern matching primitives. The formalism provides also a new method for deciding if a set of patterns subsumes a given pattern and thus, for checking the presence of useless patterns or the completeness of a set of patterns.Comment: Added appendix with proofs and extended example

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Design approaches in technology enhanced learning

Design is a critical to the successful development of any interactive learning environment (ILE). Moreover, in technology enhanced learning (TEL), the design process requires input from many diverse areas of expertise. As such, anyone undertaking tool development is required to directly address the design challenge from multiple perspectives. We provide a motivation and rationale for design approaches for learning technologies that draws upon Simon's seminal proposition of Design Science (Simon, 1969). We then review the application of Design Experiments (Brown, 1992) and Design Patterns (Alexander et al., 1977) and argue that a patterns approach has the potential to address many of the critical challenges faced by learning technologists