Recovering Sequence Diagrams from Object-oriented Code

Software modernization is a current research area in the software industry intended to transform an existing software system to a new one satisfying new demands. The initiative Architecture-Driven Modernization (ADM) helps software developers in tackling reverse engineering, software evolution and, software modernization in general. To support modernization problems, the ADM Task Force has defined a set of metamodels such as KDM (Knowledge Discovery Metamodel), being the Eclipse-MDT MoDisco project the official support for software modernization. We propose the application of ADM principles to provide relevant model-based views on legacy systems. We describe a framework to reverse engineering models from object-oriented code. In this context, we show how to recover UML sequence diagrams from Java code. We validate our approach by using ADM standards and MoDisco platform. Our research can be considered a contribution to the MoDisco community; MoDisco does not support reverse engineering of sequence diagrams and, on the other hand, the MoDisco KDM Discover was used and enriched to obtain the required information for recovering interaction diagrams

TRACEM - Towards a Standard Metamodel for Execution Traces in Model-Driven Reverse Engineering

Reverse engineering is a crucial stage in the software modernization process. The current techniques available in existing CASE tools provide forward engineering and limited facilities for reverse engineering, dynamic analysis in particular. The Architecture-Driven Modernization initiative has defined standards to support the modernization process in the model-driven engineering (MDE) context. Standardization increases interoperability between different tools enabling a new generation of solutions to benefit the whole industry and encourage collaboration among complementary vendors. In this paper, we present TRACEM, a metamodel to represent trace information under a standard representation. This metamodel complements a MDE framework for software modernization that aims to integrate static and dynamic analysis techniques during the reverse engineering process. This paper includes a case study that exemplifies how dynamic information combined with static information allows improving the whole reverse engineering process.XIX Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informátic

Computer-Aided Warehouse Engineering (CAWE): Leveraging MDA and ADM for the Development of Data Warehouses

During the last decade, data warehousing has reached a high maturity and is a well-accepted technology in decision support systems. Nevertheless, development and maintenance are still tedious tasks since the systems grow over time and complex architectures have been established. The paper at hand adopts the concepts of Model Driven Architecture (MDA) and Architecture Driven Modernization (ADM) taken from the software engineering discipline to the data warehousing discipline. We show the works already available, outline further research directions and give hints for implementation of Computer-Aided Warehouse Engineering systems

Modernizing science&engineering software systems

As the demands for modernized legacy systems rise, so does the need for frameworks for information integration and tool interoperability. The Object Management Group (OMG) has adopted the Model Driven Architecture (MDA), which is an evolving conceptual architecture that aligns with this demand. MDA could help solve coupling problems of multidisciplinary character in science and engineering that consist of one or more applications, supported by one or more platforms. The objective of this paper is to describe rigorous techniques to control the evolution from science & engineering software legacy systems to MDA technologies. We propose a rigorous framework to reverse engineering code in the context of MDA. Considering that validation, verification and consistency are crucial activities in the modernization of systems that are critical to safety, security and economic profits, our approach emphasizes the integration of MDA with formal methods

MAMBA: A Measurement Architecture for Model-Based Analysis

Model-based measurement techniques are relevant in the field of software analysis. Several meta models for the specification of quantitative measures have been proposed. However, they often focus either on static or dynamic aspects of a software system. Nevertheless, considering reengineering activities often both dimensions reveal valuable complementary insights. Existing meta models are also frequently bound to specific modeling languages, redefine underlying concepts for any new meta model, or provide only limited tool support for the automated computation of measurements from modeled measures. We present MAMBA, an integrated measurement architecture for model-based analysis---both static and dynamic---of software systems, that can be specified by arbitrary Ecore-based modeling languages. MAMBA extends the Structured Metrics Meta-Model (SMM) by additional modeling features, such as arbitrary statistical aggregate functions and periodic aggregate functions, e.g., for dynamic analysis at runtime. To consider measurements for querying system models, we outline the MAMBA Query Language (MQL) that employs SMM measures. Furthermore, we provide tool support that applies the measures specified in an (extended) SMM model and can integrate raw measurements provided by arbitrary static and dynamic analysis tools to produce the desired measurement model. We demonstrate the applicability of the approach based on three evaluation scenarios from different contexts: migration of software systems into the cloud, model-based engineering of railway control systems, and dynamic analysis for model-driven software modernization

Comparison of DC motor speed control performance using fuzzy logic and model predictive control method

The main target of this paper is to control the speed of DC motor by comparing the actual and the desired speed set point. The DC motor is designed using Fuzzy logic and MPC controllers. The comparison is made between the proposed controllers for the control target speed of the DC motor using square and white noise desired input signals with the help of Matlab/Simulink software. It has been realized that the design based on the fuzzy logic controller track the set pointwith the best steady state and transient system behavior than the design with MPC controller. Finally, the comparative simulation result prove the effectiveness of the DC motor with fuzzy logic controller

H2 optimal and μ-synthesis design of quarter car active suspension system

Better journey comfort and controllability of automobile are pursued via car industries with the aid of considering using suspension system which plays a very crucial function in handling and ride comfort characteristics. This paper presents the design of an active suspension of quarter automobile system using robust H2 optimal controller and robust μ - synthesis controller with a second order hydraulic actuator. Parametric uncertainties have been additionally considered to model within the system. Numerical simulation become completed to the designed controllers. Results display that during spite of introducing uncertainties, the designed μ - synthesis controller improves ride consolation and road protecting of the automobile while as compared to the H2 optimal controller

Towards maintainer script modernization in FOSS distributions

Free and Open Source Software (FOSS) distributions are complex software systems, made of thousands packages that evolve rapidly, independently, and without centralized coordination. During packages upgrades, corner case failures can be encountered and are hard to deal with, especially when they are due to misbehaving maintainer scripts: executable code snippets used to finalize package configuration. In this paper we report a software modernization experience, the process of representing existing legacy systems in terms of models, applied to FOSS distributions. We present a process to define meta-models that enable dealing with upgrade failures and help rolling back from them, taking into account maintainer scripts. The process has been applied to widely used FOSS distributions and we report about such experiences

Comparison of neural network NARMA-L2 model reference and predictive controllers for nonlinear quarter car active suspension system

Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. . As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system

Nonlinear autoregressive moving average-L2 model based adaptive control of nonlinear arm nerve simulator system

This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30th 202