This study developed a simulation-based E-learning software to enhance the learning outcomes of aluminum alloy bending experiments for engineering mechanics students. The bending process was modeled using simulation s...
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Mobile applications have become ubiquitous and are essential in various aspects of life. Developing cross-platform applications represents a significant challenge in mobile application development. Ionic is a software...
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Cloud-native systems represent a significant leap in constructing scalable, large systems, employing microservice architecture as a key element in developing distributed systems through self-contained components. Howe...
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ISBN:
(纸本)9798350395693;9798350395686
Cloud-native systems represent a significant leap in constructing scalable, large systems, employing microservice architecture as a key element in developing distributed systems through self-contained components. However, the decentralized nature of these systems, characterized by separate source codes and deployments, introduces challenges in assessing system qualities. Microservice-based systems, with their inherent complexity and the need for coordinated changes across multiple microservices, lack established best practices and guidelines, leading to difficulties in constructing and comprehending the holistic system view. This gap can result in performance degradation and increased maintenance costs, potentially requiring system refactoring. The main goal of this project is to offer maintainability assurance for microservice practitioners. It introduces an automated assessment framework tailored to microservice architecture, enhancing practitioners' understanding and analytical capabilities of the multiple system perspectives. The framework addresses various granularity levels, from artifacts to constructing holistic views of static and dynamic system characteristics. It integrates diverse perspectives, encompassing human-centric elements like architectural visualization and automated evaluations, including coupling detection, testing coverage measurement, and semantic clone identification. Validation studies involving practitioners demonstrate the framework's effectiveness in addressing diverse quality and maintainability issues, revealing insights not apparent when analyzing individual microservices in isolation.
Semiotics is the discipline that studies the signs and the cognitive process of meaning-making. As part of semiotics studies, the idea of semiospheres has been formulated, representing spheres of meaning that do not e...
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This study explores the influence of social media marketing on consumers' decisions to purchase green software and identifies key factors affecting those decisions. The findings contribute to effective marketing s...
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Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specific...
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Professional softwareengineering (SE) degree programs provide students with the education and skills needed to enter a new SE career, or take on increasing responsibility within their current career. An important met...
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ISBN:
(纸本)9798350322590
Professional softwareengineering (SE) degree programs provide students with the education and skills needed to enter a new SE career, or take on increasing responsibility within their current career. An important metric for evaluating such programs is the impact that completing the program has on postgraduate, career outcomes. Apart from hiring rates and median salaries, this is challenging to measure, because alumni survey response rates are frequently low, and without alumni feedback, insight into individual career advancement after graduation is difficult to observe. In this paper, we propose a new metric, called Career Velocity, that measures the impact of a degree program on alumni promotion into senior positions. The metric requires tracing alumni directory information, consisting of a person's full name, degree name, and graduation year, to public data that includes employment histories, before computing the number of months prior to promotion into a senior SE position. The metric was developed and evaluated on a mix of six degree programs, including undergraduate and graduate computerscience, softwareengineering and data science programs. The metric was further evaluated by assessing the impact of a graduate's number of months of industry experience prior to graduation. The results suggest that, independent of prior industry experience, specialized education that targets advancement in a specific career class, e.g., softwareengineering, leads to faster career progression than general education.
An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine lear...
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software-defined n etworking (SDN) is a promising technology for future smart grid (SG) systems, since SGs heavily rely on communication networks. With SDN, communication network part of SGs becomes programmable by ab...
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