Model-based Transformation Research Articles

Requirements Rationalization and Synthesis Enabled by Model Synchronization

In the international standard for system and software engineering ISO/IEC/IEEE 15288: 2015, the output of the stakeholder needs and the business or mission analysis technical processes are transformed into a technical view of the system by the system requirements definition process. In model-based systems engineering, functional needs can be modeled by use case diagrams. Intended outcomes of system requirements definition include resolution of disagreement about requirements, explicit agreement between stakeholders, and traceability. However, stakeholder needs are often elicited in a siloed manner and may be inconsistent. The lack of mathematically based systematic approaches for requirements definition poses a challenge to model-based transformation of needs into a technical view of the system that achieves agreement between stakeholders. This paper specifies and demonstrates mathematical frameworks for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rationalizing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synthesizing</i> functional needs that have been captured through an elicitation process. Benefits of this approach include but are not limited to supporting rigorous identification and resolution of disagreements and facilitating systematic analysis of change impact to achieve stakeholder agreement all with minimal intervention by the system engineers.

Extracting SBVR Business Vocabularies from UML Use Case Models Using M2M Transformations Based on Drag-and-Drop Actions

In the domain of model-driven system engineering, model-to-model (M2M) transformations present a very relevant topic because they may provide much-needed automation capabilities to the whole CASE-supported system development life cycle. Nonetheless, it is observed that throughout the whole development process M2M transformations are spread unevenly; in this respect, the phases of Business Modeling and System Analysis are arguably the most underdeveloped ones. The main novelty and contributions of this paper are the presented set of model-based transformations for extracting well-structured SBVR business vocabularies from visual UML use case models, which utilizes M2M transformation technology based on the so-called drag-and-drop actions. The conducted experiments show that this new development provides the same transformation power while introducing more flexibility to the model development process as compared to our previously developed approach for (semi-)automatic extraction of SBVR business vocabularies from UML use case models.

Metamodel based approach to generate user interface mockup from UML class diagram

Abstract Interface and interaction design take up most of the times in software creation. Human Computer Interaction (HCI) is mainly the best way to interact with a computer. During software development, class diagram is the only required step and process to design an information system according to UML notation. In addition, creating class diagram is a mandatory activity during the software creation process. The aim of this paper is to automatically generate the HCI mockup from class diagram to make easy and extremely fast software design. For example, the SEF (Schema d’Enchainement de Fenetre), is an interface model allowing to create a new interface mockup. It offers more widgets to design the HCI. And MACAO (Methode d’Analyse et de Conception d’Applications Orientees-objets) is one of the methods which helps computer scientist to easily create software. The model transformation is based on Model Driven Engineering (MDE). We use UML notation to design the class diagrams. We also adopt MDA architecture to create the model-based transformation process. Transformation rules are created using Atlas Transformation Language (ATL) to automatically generate a specific interface from the class diagram.

Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing

Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development.

Model-based random forests for ordinal regression.

We study and compare several variants of random forests tailored to prognostic models for ordinal outcomes. Models of the conditional odds function are employed to understand the various random forest flavours. Existing random forest variants for ordinal outcomes, such as Ordinal Forests and Conditional Inference Forests, are evaluated in the presence of a non-proportional odds impact of prognostic variables. We propose two novel random forest variants in the model-based transformation forest family, only one of which explicitly assumes proportional odds. These two novel transformation forests differ in the specification of the split procedures for the underlying ordinal trees. One of these split criteria is able to detect changes in non-proportional odds situations and the other one focuses on finding proportional-odds signals. We empirically evaluate the performance of the existing and proposed methods using a simulation study and illustrate the practical aspects of the procedures by a re-analysis of the respiratory sub-item in functional rating scales of patients suffering from Amyotrophic Lateral Sclerosis (ALS).

A model-based transformation framework for designing and analyzing wireless sensor networks

Wireless sensor networks (WSNs) comprise resource-constrained (e.g., memory, processing, and energy) sensor nodes that are deployed in different areas and are able to monitor environmental conditions. Simulation has been widely used in order to evaluate the network performance. In this context, software designers need to evaluate, refine, and validate high-level models for WSN applications at early stages of development via simulation tools, in particular for WSN applications supporting energy-harvesting devices. In the present paper, we propose a model-based transformation framework that allows the modeling and simulation of a WSN system supporting energy-harvesting capabilities. In this proposal, we start from a high-level specification based on the UML/MARTE profile, which describes an energy-harvesting WSN node. Then a model-to-text (M2T) transformation allows us to generate simulation scripts for analysis purposes by focusing on energy consumption.

Multi-party business process compliance monitoring through IoT-enabled artifacts

Monitoring the compliance of the execution of multi-party business processes is a complex and challenging task: each actor only has the visibility of the portion of the process under its direct control, and the physical objects that belong to a party are often manipulated by other parties. Because of that, there is no guarantee that the process will be executed — and the objects be manipulated — as previously agreed by the parties.The problem is usually addressed through a centralized monitoring entity that collects information, sent by the involved parties, on when activities are executed and the artifacts are altered. This paper aims to tackle the problem in a different and innovative way: it proposes a decentralized solution based on the switch from control- to artifact-based monitoring, where the physical objects can monitor their own conditions and the activities in which they participate.To do so, the Internet of Things (IoT) paradigm is exploited by equipping physical objects with sensing hardware and software, turning them into smart objects. To instruct these smart objects, an approach to translate classical Business Process Model and Notation (BPMN) process models into a set of artifact-centric process models, rendered in Extended-GSM (E-GSM) (our extension of the Guard-Stage-Milestone (GSM) notation), is proposed.The paper presents the approach, based on model-based transformation, demonstrates its soundness and correctness, and introduces a prototype monitoring platform to assess and experiment the proposed solution. A simple case study in the domain of advanced logistics is used throughout the paper to exemplify the different parts of the proposal.

Learning gene regulatory networks from next generation sequencing data.

In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model-based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.

Estimation of Pulse Transit Time as a Function of Blood Pressure Using a Nonlinear Arterial Tube-Load Model.

pulse transit time (PTT) varies with blood pressure (BP) throughout the cardiac cycle, yet, because of wave reflection, only one PTT value at the diastolic BP level is conventionally estimated from proximal and distal BP waveforms. The objective was to establish a technique to estimate multiple PTT values at different BP levels in the cardiac cycle. a technique was developed for estimating PTT as a function of BP (to indicate the PTT value for every BP level) from proximal and distal BP waveforms. First, a mathematical transformation from one waveform to the other is defined in terms of the parameters of a nonlinear arterial tube-load model accounting for BP-dependent arterial compliance and wave reflection. Then, the parameters are estimated by optimally fitting the waveforms to each other via the model-based transformation. Finally, PTT as a function of BP is specified by the parameters. The technique was assessed in animals and patients in several ways including the ability of its estimated PTT-BP function to serve as a subject-specific curve for calibrating PTT to BP. the calibration curve derived by the technique during a baseline period yielded bias and precision errors in mean BP of 5.1 ± 0.9 and 6.6 ± 1.0 mmHg, respectively, during hemodynamic interventions that varied mean BP widely. the new technique may permit, for the first time, estimation of PTT values throughout the cardiac cycle from proximal and distal waveforms. the technique could potentially be applied to improve arterial stiffness monitoring and help realize cuff-less BP monitoring.

Polar Business Design

Polar business design aims to enable entrepreneurs, managers, consultants, researchers, and business students to better tackle model-based analysis, creation, and transformation of businesses, ventures, and, more generically, collective endeavors of any size and purpose. It is based on a systems-thinking approach that builds on a few interrelated core concepts to create holistic visual frameworks. These core concepts act as poles linked by meaningful dyads, flows, and faces arranged in geometric shapes. The article presents two such polar frameworks as key findings in an ongoing analytic autoethnography: the three-pole Value−Activity−Stakeholder (VAS) triquetra and the four-pole Offer−Creation−Character−Stakeholder (OCCS) tetrahedron. The VAS triquetra is a more aggregated model of collective endeavors. The OCCS tetrahedron makes a trade-off between a steeper learning curve and deeper, richer representation potential. This article discusses how to use these two frameworks as well as their limits, and explores the potential that polar business design offers for future research.

Bridging the gap between requirements and design: An approach based on Problem Frames and SysML

The relation between the requirements specification and the design has been widely investigated with the aim to bridge the gap between the two artifacts. The goal is to find effective mechanisms to generate the system design starting from the analysis and specification of the requirements.This paper contributes to this research stream with an approach to create early design models from requirement artifacts. The approach weaves together the analysis and design phases favoring a tight collaboration between analysts and designers. It is based on Problem Frames, decomposition and re-composition patterns and supported by the System Modeling Language. The proposed solution has the potentiality of easing the development, shortening the development cycle and reducing the associated cost.The proposed design generation guidelines have been implemented as ATLAS Transformation Language rules in a model-based transformation process. The entire approach is model driven, allowing for the generation of the design model through transformations applied to the requirements model. The design model is automatically generated through the application of the transformation rules described in the paper. The proposed rules are fairly general and can be applied to any analysis model built according to the proposed analysis guidelines. The transformation process can be easily re-implemented using any suitable modeling tool that includes the ATLAS Transformation Language interpretation engine.

A Control Approach for Actuated Dynamic Walking in Biped Robots

This paper presents an approach for the closed-loop control of a fully actuated biped robot that leverages its natural dynamics when walking. Rather than prescribing kinematic trajectories, the approach proposes a set of state-dependent torques, each of which can be constructed from a combination of low-gain spring-damper couples. Accordingly, the limb motion is determined by interaction of the passive control elements and the natural dynamics of the biped, rather than being dictated by a reference trajectory. In order to implement the proposed approach, the authors develop a model-based transformation from the control torques that are defined in a mixed reference frame to the actuator joint torques. The proposed approach is implemented in simulation on an anthropomorphic biped. The simulated biped is shown to converge to a stable, natural-looking walk from a variety of initial configurations. Based on these simulations, the mechanical cost of transport is computed and shown to be significantly lower than that of trajectory-tracking approaches to biped control, thus validating the ability of the proposed idea to provide efficient dynamic walking. Simulations further demonstrate walking at varying speeds and on varying ground slopes. Finally, controller robustness is demonstrated with respect to forward and backward push-type disturbances and with respect to uncertainty in model parameters.

On transformation of authorization policies

In this paper, we propose a logic based approach to specify and to reason about transformation of authorization policies. The authorization policy is specified using a policy base which comprises a finite set of facts and access constraints. We define the structure of the policy transformation and employ a model-based semantics to perform the transformation under the principle of minimal change. Furthermore, we extend model-based semantics by introducing preference ordering to resolve possible conflicts during transformation of policies. We also discuss the implementation of the model-based transformation approach and analyse the complexity of the algorithms introduced. Our system is able to represent both implicit and incomplete authorization requirements and reason about nonmonotonic properties.

Integrating model-based optimization and program transformation to generate efficient parallel programs

The paper gives an overview on the DSPL programming environment, an integrated approach to automate system design and implementation of applications run on dedicated parallel systems. The programming environment consists of a data-flow language and an integrated set of tools. The tools automatically derive a software model from the given application program. Based on the model, the design decisions as the network topology, the task mapping and schedule as well as the optimal use of buffers are computed. Finally, the design decisions are automatically implemented by transforming the application program in executable code for the chosen processor network. The DSPL programming environment integrates model-based optimization techniques and program transformation techniques. The integration allows to include new aspects in the optimization process. Especially optimizations crucial to the semantics of the program can be included. The most important examples of such optimizations are the enforcement of the schedule in case of data-dependent execution of tasks and the transformation of buffered communication to unbuffered communication. Both aspects are crucial to the generation of efficient parallel implementations. The integration of the two aspects is supported by a formal framework. This allows to formally prove the correctness of the program optimizations performed by the programming environment.

Application of model-based transformation for seismic interpretational analysis

A new method for seismic interpretational analysis is proposed. The method is based on the model-based transformation (MBT) described by Nass (1989). This transformation was originally proposed as a useful tooI for noise analysis and for improving signal-to-noise ratio on common midpoint (CMP) data. The latter was achieved by optimized muting applied to traces of transformed CMP gathers. In this paper a different application of MBT for detailed seismic interpretational analysis will be discussed and demonstrated using field data examples.

Application of model-based transformations for seismic interpretational analysis

Application of model-based transformations for seismic interpretational analysis