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 research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to te...
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In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as l...
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In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality softwaresystems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit
The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in softwareengineering. We developed an intelligent system that adopts Natural Lang...
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The proceedings contain 10 papers. The topics discussed include: resource-oriented middleware abstractions for pervasive computing;requirements for a comprehensive and automated web reputation monitoring system: first...
ISBN:
(纸本)9780769547169
The proceedings contain 10 papers. The topics discussed include: resource-oriented middleware abstractions for pervasive computing;requirements for a comprehensive and automated web reputation monitoring system: first iteration;empirical study evaluating business process modeling on multi-touch devices;patentability of software;software governance using retrospectives: a case study;requirements reuse: the state of the practice;requirements reuse: the state of the practice;safety process patterns: demystifying safety standards;software modeling from life-cycle perspective;and explaining embedded software modeling decisions.
software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault predic...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault prediction in SOS. It uses supervised machine learning algorithms such as random forest, decision tree, and support vector machine to improve error prediction accuracy. Feature extraction is used for more accurate analysis. The study highlights the strengths and weaknesses of these algorithms, providing insights into the prediction of malicious software in SOS. It aims to provide high-performance and reliable software architecture, and advance fault prediction models in SFP.
Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in th...
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Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand process...
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The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily...
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software-related carbon dioxide emissions from the information and communications technology sector currently account for up to 3.9% of global emissions. With the increasing use of Machine Learning (ML) systems, this ...
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ISBN:
(纸本)9783031783852;9783031783869
software-related carbon dioxide emissions from the information and communications technology sector currently account for up to 3.9% of global emissions. With the increasing use of Machine Learning (ML) systems, this percentage of global emissions is estimated to grow. In this keynote, we embark on an interdisciplinary journey to explore the environmental sustainability of ML systems. Following a softwareengineering perspective, we see how to track and report green ML metrics in order to enable both their measurement and transparency. We then continue to optimize the carbon emissions and cost of ML systems during different stages of their lifecycle process by using green software tactics.
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