This research investigates the application of ChatGPT in softwareengineering, focusing on unit testing and code debugging, using the Design Science Research methodology. Two artefacts have been developed to evaluate ...
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Compared with traditional computing devices, IoT devices are often smaller in size, resulting in weaker battery power, memory, and computing power. Additionally, IoT devices have a higher frequency of data exchange an...
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software development is always characterized by certain parameters. In the context of Global software Development (GSD), one of the key challenges for software developers is predicting the development effort of a soft...
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Due to immense growth in digital technology and high-end intelligence algorithms, the software industry has grown immensely in a multi-fold manner over the years. This immense growth involves thousands of software eng...
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This paper introduces a lightweight, configurable interface, bus can be cascaded expansion of the airborne electromechanical interface unit. The device is applied to the electrical control system of small and medium-s...
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In a world where efficiency and reusability are the bread and butter of software development, we often find ourselves in a loop seeking better implementations of software design patterns. We as humans try to make our ...
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Teaching the Unified Modelling Language (UML) is a critical task in the frame of softwareengineering courses. Teachers need to understand the students' behavior along with their modeling activities to provide sug...
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ISBN:
(纸本)9798350359329;9798350359312
Teaching the Unified Modelling Language (UML) is a critical task in the frame of softwareengineering courses. Teachers need to understand the students' behavior along with their modeling activities to provide suggestions and feedback to avoid more frequent mistakes and improve their capabilities. This paper presents a novel approach for teaching the UML in softwareengineering courses, focusing on understanding and improving student behavior and capabilities during modeling activities. It introduces a cloudbased tool that captures and analyzes UML diagrams created by students during their interactions with a UML modeling tool. The key aspect of the proposal is the integration of a Retrieval Augmented Generation Large Language Model (RAG-based LLM), which generates insightful feedback for students by leveraging knowledge acquired during the modeling process. The effectiveness of this method is demonstrated through an experiment involving a substantial dataset comprising 5,120 labeled UML models. The validation process confirms the performance of the UML RAG-based LLM in providing relevant feedback related to entities and relationships in the students' models. Additionally, a qualitative analysis highlights the user satisfaction, underscoring its potential as a valuable tool in enhancing the learning experience in software modeling education.
The computing First Network (CFN) is a novel network paradigm that efficiently allocates and schedules computing force, storage resources, and network capacity across cloud, edge, and device domains. Leveraging the co...
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Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many...
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ISBN:
(纸本)9798400706585
Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevators, by proposing an approach called QUELL. In QUELL, we use QELM for the waiting time prediction related to the scheduling software of elevators, with applications for software regression testing, elevator digital twins, and real-time performance prediction. The scheduling software is a classical software implemented by our industrial partner Orona, a globally recognized leader in elevator technology. We assess the performance of QUELL with four days of operational data of a real elevator installation with various feature sets and demonstrate that QUELL can efficiently predict waiting times, with prediction quality significantly better than that of classical ML models employed in a state-of-the-practice approach. Moreover, we show that the prediction quality of QUELL does not degrade when using fewer features. Based on our industrial application, we further provide insights into using QELM in other applications in Orona, and discuss how QELM could be applied to other industrial applications.
Requirement engineering plays an essential role in producing successful software products where multiple stakeholders are involved. It is imperative to elicit stakeholders' emotional goals and functional and quali...
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