Testing of model-driven development applications

This article presents the process Automatic source code generation for ChatBot using the Model Driven Development (MDD) for Facebook Messaging platform. We propose the use of metamodel to define the ChatBot, including the option of consuming external REST services. Two test scenarios are presented: the first one is a ChatBot oriented to the sales of pizzas and hamburgers, which requires to obtain external information (REST services) on the ingredients and the search for commercial establishments that satisfy the options defined by the user. Our second test scenario is aligned with elements of IoT (Internet Of Things), specifically manages the sensors of a Sense Hat on a RaspBerry Pi 3 card, considering sensors such as: humidity, temperature, pressure, accelerometer and an actuator represented by a Led matrix. Finally, the results of each of the ChatBot are shown. these are automatically generated from the conceptual specification. The Chatbots are deployed on the Facebook platform validating the consumption of information from external REST services.

This study examines the relationship between digital transformation and human resource management (HRM) practices in public universities in the United Arab Emirates via a synthesis of Resource-Based View and Dynamic Capabilities Theory. Despite the growing interest in digital transformation, limited research has examined its implications within public universities in the United Arab Emirates. Adopting a conceptual approach, this paper provides a comprehensive framework that connects the important elements of digital transformation, comprising technological infrastructure, digital transformation culture, and human resource capabilities, to the core HRM practices, namely recruitment, training, and performance appraisal. This study contributes to the existing literature by proposing a dual conceptualization of digital transformation as both a strategic resource and an adaptive capability that enhances HRM effectiveness. Moreover, the current study proposes a structured questionnaire approach for future empirical validation using Structural Equation Modelling (SEM). It is expected that future empirical findings will demonstrate that it is not only the size of technology investment, but also the organization’s ability to develop dynamic capabilities like organizational learning and strategic flexibility that mediate the relationship between digital transformation and HRM practices. Despite the growing interest in digital transformation, limited research has examined its implications within public universities in the United Arab Emirates. In particular, little is known about the underlying mechanisms through which digital transformation influences HRM practices, especially the mediating role of dynamic capabilities. Furthermore, the integration of the Resource-Based View and Dynamic Capabilities Theory in explaining this relationship remains underexplored in higher education contexts. Accordingly, this study aims to develop a conceptual framework that explains the relationship between digital transformation and HRM practices in UAE public universities. The study draws theoretical implications through the integration of RBV and the dynamic capabilities perspective into the ecosystem domain of digital HRM, as well as for university leaders and policymakers who would design systematic processes to align digital transformation initiatives in a way that serves human resource strategies. It highlights the need to create a digital culture, develop workforce digital competencies, and take an integrated approach to build sustainable institutional performance.

The implementation of lean production systems is deemed essential for companies that wish to obtain high levels of competitiveness. There are several examples in the literature discussing the adoption of lean roadmaps; however, since such maps emphasise technical factors of lean change, the organisational learning (OL) process and human resources management (HRM) practices tend to be neglected. This article presents a method for assessing the impact of HRM practices and OL factors in a company under lean implementation. The method incorporates concepts of HRM and OL into lean implementation roadmaps, enabling a maturity analysis regarding dimensions of OL at different contextualisation levels, and allowing the proposition of improvements in HRM practices. The proposed method is illustrated in a case study from the automotive parts manufacturing sector.

PurposeThe purpose of this paper is to establish if indeed human resource management (HRM) practices drive organizational learning capability and, in turn, fortify small and medium enterprises (SMEs) performance. The purpose of mediation analysis is to see if the influence of organizational learning capability is stronger than the direct influence of HRM practices on SME performance.Design/methodology/approachData was collected from managerial-level employees of SMEs using questionnaire survey. This study used the Partial Least Squares approach to structural equation modeling to test the hypothesized relationship, as it involves the relationships among multiple variables.FindingsOf the HRM practices, incentive and compensation and team working seem to be important for firm performance. As for organizational learning capability, only openness and experimentation and managerial commitment have a direct positive impact on firm performance. The results of the mediation analysis established organizational learning capability as a mediator, albeit partially.Originality/valueAlthough prior research revealed a positive relationship between HRM practices and performance, this study illuminates the black box in-between, as few studies have established its importance in the relationship between HRM practices and firm performance. The findings provide more consensus on the ongoing debate on the linkages among HRM practices, SME performance and organizational learning capability.

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.

Based on our experience of collaborating with industry, we observed that test case generation usually relies on test case specifications (TCSs), commonly written in natural language, specifying test cases of a System Under Test at a high level of abstraction. In practice, TCSs are commonly used by test engineers as reference documents to perform these activities: 1) Manually executing test cases in TCSs; 2) Manually coding test cases in a test scripting language for automated test case execution. In the latter case, the gap between TCSs and executable test cases has to be filled by test engineers, requiring a significant amount of coding effort and domain knowledge. Motivated by the above observations from the industry, we first propose, in this paper, a TCS language, named as Restricted Test Case Modeling (RTCM), which is based on natural language and composed of an easy-to-use template, a set of restriction rules and keywords. Second, we propose a test case generation tool (aToucan4Test), which takes TCSs in RTCM as input and generates either manual test cases or automatically executable test cases, based on various coverage criteria defined on RTCM. To assess the applicability of RTCM, we manually modeled two industrial case studies and examined 30 automatically generated TCSs. To evaluate aToucan4Test, we modeled three subsystems of a Video Conferencing System developed by Cisco Systems, Norway and automatically generated executable test cases. These test cases were successfully executed on two commercial software versions. In the paper, we also discuss our experience of applying RTCM and aToucan4Test in an industrial context and compare our approach with other model-based testing methodologies.

<i>Managing Human Resources in China: The View from Inside Multinationals</i>, by Yu Zheng. Cambridge: Cambridge University Press, 2013. xx+287 pp. US$110.00 (cloth), US$49.99 (paper), US$36.00 (eBook).

How can human resource management (HRM) practices promote entrepreneurial performance in small and medium‐sized enterprises (SMEs)? This article discusses the association between human capital management (HCM) and other contemporary HRM practices and the ability of SMEs to be entrepreneurial. In a study of 99 SMEs, HRM practices that promote employee discretionary behavior, knowledge sharing, and organizational learning are found to be positively associated with entrepreneurial performance. Two contingencies are also identified for this relationship. First, the use of strategic HCM practices enhances the observed positive association. Second, these relationships are strongest for SMEs operating in high‐technology industries. © 2004 Wiley Periodicals, Inc.

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.

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.

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.

The success of software development projects is still relatively small when compared to others in the field of project success. The quality and speed of software project is highly dependent on the human resource. Therefore in this paper will discuss the improvement of the collaboration model on construction phase of software development in order to obtain the quality of the product (software) better. The method of this collaborative model development is made with the Socio-Technical Systems (STS) approach, which divides a collaborative system into technical system and socio-technical systems that affect each other. The results of this research proposed a collaborative model in construction phase of software and their indicator.

Risk identification, as a foundational step in risk management, is essential for the success of software development projects, including those using the Scrum methodology. However, risk management in Scrum often lacks structured practices, relying instead on the iterative and collaborative nature of the methodology. This paper presents a novel risk identification framework tailored for Scrum projects, leveraging Scrum artifacts and their associated attributes to enhance risk detection and analysis. The framework encompasses automated data collection from project management environments, such as Jira, thus minimizing the effort required from the project team for risk-related data gathering. By monitoring deviations in artifact attributes and applying predefined thresholds, the framework facilitates the detection of risks at any point in the project lifecycle. Experimental evaluation across three distinct Scrum projects demonstrated the framework’s applicability in identifying a wide range of risks, including long-term ones. The proposed framework contributes to Agile project management by offering a structured, automated approach to risk identification, fostering a culture of proactive risk management within Scrum teams.

Understanding the relationship of conflict and success in software development projects

The effectiveness of system analysis and design methodologies plays a pivotal role in the success of software development projects. This research conducts a comparative analysis of various methodologies, including the Waterfall Model, Spiral Model, Prototyping Model, Iterative Model, Unified Process, Object-Oriented Analysis and Design, Joint Application Design, Computer Aided Software Engineering Tools, Rapid Application Development / Rapid Systems Development, Feature- Driven Development, Extreme Programming, Agile Method, and DevOps, to explore their respective strengths and considerations. The study examines key aspects such as flexibility, documentation, risk management, complexity, and suitability across these methodologies. Findings reveal that each methodology offers unique advantages and challenges, influencing their applicability in different project contexts. The research underscores the importance of aligning methodology choices with project-specific requirements, organizational culture, and environmental factors to optimize software development outcomes. Moreover, it suggests that hybrid approaches, combining elements from multiple methodologies, may offer a balanced approach to address diverse project needs and maximize effectiveness. By providing insights into the comparative effectiveness of system analysis and design methodologies, this research contributes to informed decision- making and improved project success in software development endeavors.