In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochast...
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software reuse techniques have become a pervasive research hotspot in today's softwareengineering field. The most researched and widely applied is combinatorial technology, which is software reuse technology base...
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Cybersecurity is the process of defending computer networks, systems, and digital data from intrusions, hacks, damage, and other cyber threats. It also prevents illegal access, and exploitation of sensitive and person...
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Testing large software applications poses a major challenge, especially in the presence of changes that are usually introduced after the initial deployment of said applications. This makes it rather difficult to antic...
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Federal learning is an effective distributed learning technology that allows machine learning model training while protecting data privacy. However, with the increase of the number of user -side devices, the calculati...
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FDSE serves as an automatic test generation tool designed for C programs based on symbolic execution. FDSE employs fuzzing-based pre-analysis and combines static symbolic execution and dynamic symbolic execution to im...
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ISBN:
(纸本)9783031572586;9783031572593
FDSE serves as an automatic test generation tool designed for C programs based on symbolic execution. FDSE employs fuzzing-based pre-analysis and combines static symbolic execution and dynamic symbolic execution to improve the effectiveness of test generation. FDSE achieves 5132 scores and is ranked 4th in the branch coverage track of Test-Comp 2024.
The development of software for computer modeling of the transport and diffusion of harmful substances in the atmospheric air is a valuable contribution to environmental monitoring and forecasting. The developed softw...
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Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the ...
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The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of ...
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In agile software development, user stories capture requirements from the user's perspective, emphasizing their needs and each feature's value. Writing concise and quality user stories is necessary for guiding...
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ISBN:
(纸本)9783031783852;9783031783869
In agile software development, user stories capture requirements from the user's perspective, emphasizing their needs and each feature's value. Writing concise and quality user stories is necessary for guiding software development. Alongside user story generation, prioritizing these requirements ensures that the most important features are developed first, maximizing project value. This study explores the use of Large Language Models (LLMs) to automate the process of user story generation, quality assessment, and prioritization. We implemented a multi-agent system using Generative Pre-trained Transformers (GPT), specifically GPT-3.5 and GPT-4o, to generate and prioritize user stories from the initial project description. Our experiments on a real-world project demonstrate that GPT-3.5 handled user story generation well, achieving a higher semantic similarity score comnpared to the GPT4o. Both models showed consistent performance in prioritizing requirements, effectively identifying the core features of the application. These early results indicate that LLMs have significant potential for automating requirements analysis, particularly generating and prioritizing user stories.
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