MDD based case tool for Automatic Generation of ChatBot

Human resource management practices are key for the success of software development projects. Practices that promote knowledge sharing and organizational learning are positively related to development---effort curves, and thus software companies are looking for different alternatives oriented to promoting these practices. The model-driven development (MDD) paradigm is positioned as one of the best alternatives for reutilization of development knowledge. In particular, this paradigm considers the specification of conceptual models that can be used as input for automatic code generation to different target platforms. However, testing of applications developed through MDD solutions is still performed by the manual definition and execution of test cases by testers, which negatively impacts in the time reduction obtained from automatic code generation and the reutilization of knowledge generated during the MDD project execution. To address this issue, this paper presents a testing approach that automatically generates executable test cases for software developed by using MDD technologies.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Purpose The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances. Design/methodology/approach COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO2). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test. Findings IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO2), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials. Originality/value The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).

The low-code approach is an important area of research being developed to improve the rapid creation and performance of software applications. This approach allows developers and users to easily create software applications through an interactive interface. Existing research shows that the low-code approach accelerates the development process in innovative ways, saves effort and time, and reduces code complexity. This study analyses and compares automatic code generation and transformation techniques in low-code platforms. The study analyses the contributions of automatic code generation and transformation to software engineering processes and evaluates the impact of these techniques on software product quality and development speed. The importance of the Model-Based Development (MBD) approach in automated code generation processes is highlighted. It is stated that MBD provides benefits such as speeding up the software development process and reducing errors by enabling automatic code generation from high-level abstract models. In the study, various literature studies were evaluated to examine the impact of automatic code generation and transformation techniques on the application development process. These studies have shown how automatic code generation and transformation techniques are applied, what results are achieved and what contributions these techniques make to the software development process. The results of the study show that automatic code generation and transformation techniques have a great impact on the application development process. In particular, the acceleration of the software development process, the reduction of errors and the ability to produce more effective and efficient software products are important benefits of these techniques. In addition, it has been found that this approach increases the success of software projects by enabling software developers to produce fast and accurate solutions in complex systems. In this context, the importance of automatic code generation and transformation techniques plays an important role in software development processes.

The digital signal processing (DSP) processor-in-the-loop tests based on automatic code generation technology are studied. Firstly, the idea of model-based design is introduced, and the principle and method of embedded code automatic generation technology are analyzed by taking the automatic code generation of the DSP control algorithm for pulse width modulation (PWM) output as an example. Then, the control system model is established on MATLAB/Simulink. After verifying the model through simulation, the target board platform is established with DSP as the core processor, and the automatically generated code is tested by the processor-in-the-loop (PIL). The results show that the technology greatly shortens the development cycle of the project, improves the robustness and consistency of the control code, and can be widely used in the complex algorithm development process of the controller, from intelligent design and modeling to implementation.

The Internet of Things (IoT) is an increasingly developed field because of how it is affecting virtually all aspects of society. In this context, the amount of data and objects that integrate into this new internet is exploding and will continue to do so in the foreseeable future. One of the main challenges that the IoT is still facing is the ease of integration between applications and the general infrastructure underneath: namely a simple way of allowing horizontal integration. This paper proposes the use of a framework that allows for a direct horizontal integration between the different IoT elements that could be present within any system.

This paper describes the IOPT-Tools cloud-based tool-chain, offering a complete set of tools supporting design automation for embedded controller’s development, benefiting from adopting a model-driven development attitude. The tools are freely available online at http://gres.uninova.pt/IOPT-Tools/ . The tool-chain relies on IOPT nets (Input-Output Place-Transition nets) to describe the controller behavior, allowing an explicit representation of constraints on input and output signals and events, which is necessary for the development of controllers. The IOPT-Tools tool-chain includes tools for interactive graphical IOPT nets models editing, simulation and test (token-player, timing diagram, remote debugging), as well as a state-space generator, state-space visualization, and a query system for properties verification. The tool-chain also supports the automatic generation of execution code to be directly deployed in the controllers’ implementation platforms, such as FPGA boards, as well as Arduino, Raspberry, and other Linux-based boards. Most notably, it is possible to obtain C code and VHDL code to be directly deployed into the referred boards without writing/changing a line in the generated code. The tool-chain uses the PNML format for storing the models and can import PNML models generated by other frameworks, automatically generating, if necessary, an associated graphical representation. After, it is possible to add input and output signals and events to create controller models. IOPT-Tools also supports net operations, namely net addition, allowing composition of sub-models, and net splitting, which in conjunction with the use of clock domains and dedicated communication channels support the development of distributed controllers.

In this paper a software engineering concept is described which permits the automatic solution of a nonlinear set of network equations. The power flow equation set can be seen as a defined subset of a network equation set. The automated solution process is the numerical Newton-Raphson solution process of the power flow equations where the key code parts are the numeric mismatch and the numeric Jacobian term computation. It is shown that both the Jacobian and the mismatch term source code can be automatically generated in a conventional language such as Fortran or C. Thereby one starts from a high level, symbolic language with automatic differentiation and code generation facilities. As a result of this software engineering process an efficient, very high duality Newton-Raphson solution code is generated which allows easier implementation of network equation model enhancements and easier code maintenance as compared to hand-coded Fortran or C code.

This chapter demonstrates how high-level code is automatically generated for multi-core processors. The code generation capability of the Verifiable Embedded Real-Time Application Framework (VERTAF) was extended to support multi-core processors in the new VERTAF/Multi-Core (VMC) framework for embedded systems. After users specify embedded software requirements via SysML models along with parallel task, parallel data, and parallel dataflow specifications, the code generator automatically generates parallel code. Using the digital video recording (DVR) system as a case study, we show the correctness and advantages of the VMC code generator. The main inputs of VMC code generator include the block definition diagrams, state machine diagrams, and requirement diagrams of the system to be designed. The proposed code generation in VMC not only significantly decreases the amount of manually-written code, but also provides a formal procedure for model-conforming code generation of multi-core embedded software.

The development of avionics software demands more and more human intervention. The software development process is rather highly expensive and risky since it must undergo certification. Automation of such a process can reduce errors and human interaction. Automated development tools usually deal with automatic code generation. These approaches are actually based on pre-defined fixed rules and policies to automatically generate software code which still demands considerable hand-coded software. This paper presents a self-directed development process that is able to make decisions by its own to generate software code. The automated decision-making process relies on a reasoner, and an ontological database that captures development skills from human developers as well as the dynamic configurations and capabilities of the system under development. The knowledge captured enables highlevel reasoning to automatically generate the control software code. A case study based on the development of Embedded Control Software (ECS) for Distributed Aircraft Fuel Management (DAFM) is presented. Details of the reasoning ontology to develop ECS, and experimental results from ECS for DAFM automatically generated are presented.

In emergency response environments, variant entities with specific behaviors and interaction between them form a complex system that can be well modeled by multi-agent systems. To build such complex systems, instead of writing the code from scratch, one can follow the model-driven development approach, which aims to generate software from design models automatically. To achieve this goal, two important prerequisites are: a domain-specific modeling language for designing an emergency response environment model, and transformation programs for automatic code generation from a model. In addition, for modeling with the language, a modeling tool is required, and for executing the generated code there is a need to a platform. In this paper, a model-driven framework for developing multi-agent systems in emergency response environments is provided which includes several items. A domain-specific modeling language as well as a modeling tool is developed for this domain. The language and the tool are called ERE-ML and ERE-ML Tool, respectively. Using the ERE-ML Tool, a designer can model an emergency response situation and then validate the model against the predefined constraints. Furthermore, several model to code transformations are defined for automatic multi-agent system code generation from an emergency response environment model. For executing the generated code, an extension of JAMDER platform is also provided. To evaluate our framework, several case studies including the Victorian bushfire disaster are modeled to show the ability of the framework in modeling real-world situations and automatic transformation of the model into the code.

The Internet of Things (IoT) paradigm is employed in different sports-related activities for health monitoring and performance assessments. Athlete training based on physical activities and observation is performed using IoT devices and computing systems. Clinical Decision Support (CDS) aims to improve health and health care by providing doctors, employees, patients, or other persons with knowledge and information tailored to each individual’s needs at the right moment. The state of being physically ready to do the actions required by a particular activity (usually a sport). Sports-specific skills that can only be mastered via repeated practice. The problem of consistent performance index management is limited due to large data validations. This article introduces a Reliable Index Assessment Technique (RIAT) for evaluating athlete performances. The physical attributes, such as oxygen level, stamina, tiredness, completion time, speed, etc., are observed using wearable sensors. The observed signals are processed for their appropriate declinations and stagnancy during the training sessions. The training index is constructed based on the declinations and stagnancy identified through an intense federated learning paradigm. This index assessment relies on multilevel training updates to prevent performance assessment inconsistencies. The index construction is made from the multilevel assessment using federated learning updates. This update is validated using the previous index and the currently observed inferences for preventing computation errors. Therefore, the distributed training data is accessed and updated for global indexing through the IoT elements. This technique achieves high precision, an assessment rate under measured computation time, and fewer errors.

Automatic code generation for production automotive powertrain applications introduces many challenges for the tool vendor. Resource efficiency, reliability, and product process development issues need to be addressed. Automatic code generation used for rapid prototyping systems usually will not satisfy the requirements necessary for production oriented code generation. Some of the requirements critical to automatic code generation are not immediately obvious to a tool vendor who is not active in the user's industry. The purpose of this paper is to address some of the most common requirements for production automotive powertrain code generation.

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

The Model-Driven Development paradigm aims to represent all the information system features through models. Conceptual-Model Programming offers a similar approach, but with a focus on automatic code generation. Both approaches consider modeling and traceability of different abstraction levels, where each level can be tackled with different modeling methods. This heterogeneity introduces a challenge for the quality of the traceability and transformations among models, especially when aiming for automatic code generation. In this paper, we introduce a holistic conceptual-model programming method to generate code from different abstraction levels (from the problem space to the solution space), through three modeling languages whose consistency has been ontologically ensured by two transformation techniques. Particularly, we focus on transformations from the strategic layer using i*, to business process layer using Communication Analysis (CA), and to the system conceptual model layer with OO-Method, which can automatically generate fully functional systems. Even though there are previous works that have proposed partial transformations among these modeling methods, this paper is the first one that deals with the perspective of putting together all the models in a single development method. For each transformation, we discuss what parts can be automatically performed and what parts need human intervention.