Unprecedented improvements in connectivity, speed, and latency are anticipated with the emergence of 6G wireless technology, opening the door for game-changing applications like AI-driven automation, smart cities, and...
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softwareengineering aims to effectively translate stakeholders' requirements into executable code to fulfill their needs. Traceability from natural language use case requirements to classes in a UML class diagram...
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ISBN:
(纸本)9783031758713;9783031758720
softwareengineering aims to effectively translate stakeholders' requirements into executable code to fulfill their needs. Traceability from natural language use case requirements to classes in a UML class diagram, subsequently translated into code implementation, is essential in systems development and maintenance. Tasks such as assessing the impact of changes and enhancing software reusability require a clear link between these requirements and their software implementation. However, establishing such links manually across extensive codebases is prohibitively challenging. Requirements, typically articulated in natural language, embody semantics that clarify the purpose of the codebase. Conventional traceability methods, relying on textual similarities between requirements and code, often suffer from low precision due to the semantic gap between high-level natural language requirements and the syntactic nature of code. The advent of Large Language Models (LLMs) provides new methods to address this challenge through their advanced capability to interpret both natural language and code syntax. Furthermore, representing code as a knowledge graph facilitates the use of graph structural information to enhance traceability links. This paper introduces an LLM-supported retrieval augmented generation approach for enhancing requirements traceability to the class diagram of the code, incorporating keyword, vector, and graph indexing techniques, and their integrated application. We present a comparative analysis against conventional methods and among different indexing strategies and parameterizations on the performance. Our results demonstrate how this methodology significantly improves the efficiency and accuracy of establishing traceability links in software development processes.
In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the ...
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Intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent ***, the current trend of customizing smart campus solutions often positions campus citizens as mere ...
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The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extract...
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Bellwether analysis is a widely-used technique in softwareengineering for identifying representative projects that can be used to predict outcomes in other projects. However, existing methods, such as the one propose...
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The video gaming industry has evolved into an industry that drives high revenue and tangentially faces some of the toughest problems in the softwareengineering domain. With more games being released with either remot...
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The video gaming industry has evolved into an industry that drives high revenue and tangentially faces some of the toughest problems in the softwareengineering domain. With more games being released with either remote multiplayer components or cloud streaming foundations, maintaining an engaging player experience that lives up to a high-quality bar is a problem that many game studios must overcome. Traditionally, studios have relied on brute-force manual testing of their software to find impacting bugs. However, as studios and publishers focus on shortening the delivery timeline of their products to consumers, we have seen a rise in the use of testing tools, automation and enhanced processes that focus on achieving a high level of quality. In this systematic literature review, we provide a review of the tools and processes and discuss how these tools enhance the quality experience or help decrease the cost of testing while allowing for faster releases. We conclude by observing some potential areas of opportunity, such as suggesting an approach to help validate the severity of bugs found through community validation and providing a mechanism to create a way to track bugs across heterogeneous devices.
Traditional Direction of Arrival (DOA) estimation algorithms for coherent signals in uniform circular array (UCA), such as mode space transformation, allow for the adaptation of advanced algorithms initially developed...
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Large language models (LLMs) based on transformer architecture have revolutionized natural language processing (NLP), demonstrating excellent capabilities in understanding and generating human-like text. In software E...
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ISBN:
(纸本)9783031808883;9783031808890
Large language models (LLMs) based on transformer architecture have revolutionized natural language processing (NLP), demonstrating excellent capabilities in understanding and generating human-like text. In softwareengineering, LLMs have been applied in code generation, documentation, and report writing tasks, to support the developer and reduce the amount of manual work. In software Testing, one of the cornerstones of softwareengineering, LLMs have been explored for generating test code, test inputs, automating the oracle process or generating test scenarios. However, their application to high-level testing stages such as system testing, in which a deep knowledge of the business and the technological stack is needed, remains largely unexplored. This paper presents an exploratory study about how LLMs can support system test development. Given that LLM performance depends on input data quality, the study focuses on how to query general purpose LLMs to first obtain test scenarios and then derive test cases from them. The study evaluates two popular LLMs (GPT-4o and GPT-4o-mini), using as a benchmark a European project demonstrator. The study compares two different prompt strategies and employs well-established prompt patterns, showing promising results as well as room for improvement in the application of LLMs to support system testing.
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