Aiming at the needs of multi-agent confrontation games in the complex wide-area environment at sea, this paper proposes a multi-agent construction idea driven by knowledge rules and data learning, and designs a hybrid...
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Insights and assessments about the quality, reliability, or trustworthiness of software systems is important for many software applications. Especially for large or mission-critical software systems, reliable measures...
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ISBN:
(纸本)9781728174365
Insights and assessments about the quality, reliability, or trustworthiness of software systems is important for many software applications. Especially for large or mission-critical software systems, reliable measures and assertions are crucial. Since software repositories contain information about source code, software development processes, and team interactions, we extract the provenance of software artifacts from those repositories and store the provenance according to a provenance model defined using W3C PROV data model. We use the recorded provenance to discover insights about the software and its development process, which we apply and evaluate for a large aerospace software system.
Artificial intelligence (AI) has become a transformative force in various industries, including software development and innovation. This paper focuses on measuring the impact of two branches of AI, generative AI and ...
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ISBN:
(纸本)9789819716814
Artificial intelligence (AI) has become a transformative force in various industries, including software development and innovation. This paper focuses on measuring the impact of two branches of AI, generative AI and knowledge-based systems, in the context of software development and innovation. Generative AI involves machines generating content without explicit human programming, while knowledge-based systems leverage explicit knowledge representation and reasoning techniques. The technology has the potential to enhance software development processes, drive innovation, and improve overall efficiency. This paper explores the applications of generative AI systems in software development and innovation through case studies, real-world examples, and empirical research. Metrics such as development speed, code quality, innovation rate, and developer satisfaction are used to assess the tangible benefits of these technologies. Additionally, potential challenges and ethical considerations are discussed, along with future directions for integrating these technologies into software development practices. In this paper, we will delve into the specific applications of generative AI systems in software development and innovation. We will explore case studies, real-world examples, and empirical research to measure the impact of these technologies. We will examine metrics such as development speed, code quality, innovation rate, and developer satisfaction to assess the tangible benefits of generative AI and knowledge-based systems. Additionally, we will discuss potential challenges, ethical considerations, and future directions in leveraging these technologies for software development and innovation. By understanding and measuring the impact of generative AI systems in software development and innovation, we aim to provide insights into the potential benefits, limitations, and implications of adopting these technologies. Through this research, we hope to contribute to the ongoing
Zero-knowledge proof systems are becoming powerful tools in domains like blockchain networks. In this work, we improve abstract interpretation methods to sanity check PLONKish arithmetizations of computation, such as ...
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In recent years, architecture decision records (ADRs) have emerged as a lightweight way to capture information about software architecture, decisions, context, and consequences. ADRs have a simple structure, which mak...
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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.
Metamorphic Testing (MT) addresses the test oracle problem by defining how program outputs should change in response to specific input changes. The relations between input changes and their corresponding output change...
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ISBN:
(纸本)9798350380279;9798350380262
Metamorphic Testing (MT) addresses the test oracle problem by defining how program outputs should change in response to specific input changes. The relations between input changes and their corresponding output changes are called Metamorphic Relations (MRs). Generating suitable MRs is complex and often requires deep domain knowledge. Our previous work introduced MetaTrimmer, a test-data-driven approach for selecting and constraining MRs, involving three steps: Test Data (TD) Generation, MT Process, and MR Analysis. MR Analysis is done to decide whether the violation of an MR for a specific input data pair (original and changed) indicates a failure or simply means that the MR does not apply for the chosen inputs. In this paper, we present an association-rule-based approach that semi-automatically extracts constraints dividing the input space into valid/invalid data during the MR Analysis step of MetaTrimmer. We validate our approach using 44 methods to which six predefined MRs are applied. Our results indicate that the proposed method efficiently identifies correct input data space constraints. More studies are needed to provide additional evidence that MetaTrimmer with the enhanced MR Analysis step is scalable and generalisable.
Security requirements engineering is a manual and error-prone activity that is often neglected due to the knowledge gap between cybersecurity professionals and software requirements engineers. In this paper, we aim to...
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ISBN:
(纸本)9781665457019
Security requirements engineering is a manual and error-prone activity that is often neglected due to the knowledge gap between cybersecurity professionals and software requirements engineers. In this paper, we aim to automate the process of recommending and synthesizing security requirements specifications and therefore supporting requirements engineers in soliciting and specifying security requirements. We investigate the use of Relational Generative Adversarial Networks (GANs) in automatically synthesizing security requirements specifications. We evaluate our approach using a real case study of the Court Case Management System (CCMS) developed for the Indiana Supreme Court's Division of State Court Administration. We present an approach based on RelGAN to generate security requirements specifications for the CCMS. We show that RelGAN is practical for synthesizing security requirements specifications as indicated by subject matter experts. based on this study, we demonstrate promising results for the use of GANs in the software requirements synthesis domain. We also provide a baseline for synthesizing requirements, highlight limitations and weaknesses of RelGAN and define opportunities for further investigations.
Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL)...
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ISBN:
(纸本)9798350368529;9798350368512
Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. based on the workflow, we design and implement an STP toolbox called UCTB (Urban Computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal domain knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://***/uctb/UCTB.
Guideline knowledge is helpful for LLM (Large Language Model) based Agent in embody task planning. In this work, we introduce Textgrad to optimize the Guideline knowledge for the agent's embodied tasks. This allow...
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