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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

共 17325 条记录，以下是131-140 订阅

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MDASC: Advanced Dual-Layered Code Cloning Technique for Identifying Reused Malicious Code 36

MDASC: Advanced Dual-Layered Code Cloning Technique for Iden...

引用

36th International conference on software engineering and knowledge engineering, SEKE 2024

作者： Glani, Yasir Ping, Luo Ke, Lin School of Software Tsinghua University China

ISBN: (纸本)1891706594

Code cloning is a prevalent practice in software development, frequently exploited by adversaries to propagate malicious code, compromising user security and privacy. Recently proposed detection techniques often fail to identify complex clones, posing significant s oftware i ntegrity a nd s ecurity risks. In response, we introduce MDASC, an advanced dual-layered approach to detecting known Android malware through code cloning. MDASC employs method-based signature detection followed by sliding windows-based detection, effectively identifying modified and unmodified states of reused malware code, encompassing Type-I, Type-II, and Type-III (VST, ST, and MT) clones. Our comprehensive comparative study evaluates MDASC against recently proposed techniques across various code cloning detection methods, demonstrating its superiority in precision, recall, F1-score, accuracy, and efficiency. Leveraging intelligent retrieval and processing algorithms, MDASC maintains high efficiency, achieving an average detection time of 0.33 seconds for a database of 132,414 files and 13.5 million lines of code. This efficiency and effectiveness emphasize MDASC’s potential for real-time security applications. MDASC represents a significant a dvancement in malicious code reuse detection, offering unparalleled accuracy and efficiency, t hereby e nhancing s oftware s ecurity practices within the rapidly evolving Android ecosystem. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Android malware

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Multi-agent Hybrid Architecture Design for Naval Warfare Game 3rd

Multi-agent Hybrid Architecture Design for Naval Warfare Gam...

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3rd International conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Li, Xing Zhang, Pengyi Gu, Qijia Su, Jian Li, Jingyi CSSC Systems Engineering Research Institute Beijing China

ISBN: (纸本)9789819711062

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-driven multi-agent software framework. The overall software framework is modularized based on knowledge rules, and core action decisions are constructed based on data learning methods. In the end, a multi-agent instance was built, and the counterparty was built for confrontation verification in the sea scene. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Learning systems

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Analyzing software engineering Processes with Provenance-based knowledge Graphs

Analyzing Software Engineering Processes with Provenance-bas...

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IEEE Aerospace conference (AeroConf)

作者： Schreiber, Andreas von Kurnatowski, Lynn de Boer, Claas German Aerosp Ctr DLR Inst Software Technol D-51147 Cologne Germany German Aerosp Ctr DLR Inst Software Technol D-82234 Wessling Germany Tech Univ Dresden Inst Software & Multimedia Technol D-01062 Dresden Germany

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.

关键词： COVID-19 Training Analytical models Pandemics Unified modeling language W3C software systems

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Measuring Impact of Generative AI in software Development and Innovation 5th

Measuring Impact of Generative AI in Software Development an...

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5th International conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Kumar, Tajinder Garg, Vishal Lalar, Sachin Kumar, Rajinder CSE Department JMIETI Haryana Radaur Yamunanagar135133 India DCSA Kurukshetra University Haryana Kurukshetra136119 India Computer Science and Engineering Punjabi University Punjab Patiala147002 India

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

关键词： knowledge representation

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Abstract Interpretation for PLONKish Circuits 34

Abstract Interpretation for PLONKish Circuits

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34th Annual International conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Soureshjani, Fatemeh Heidari Gorzny, Jan Polytechnique Montréal Computer Engineering and Software Engineering Department Canada Zircuit Canada

ISBN: (纸本)9798331504830

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 those used in the Halo2 zero-knowledge proof system. Our work aims to improve the accuracy of checks for unused gates, assigned but unconstrained values, and possibly under-constrained circuits, all of which may indicate a bug in the circuits. We show how copy constraints and arbitrary lookup functions can be used in abstract interpretation based analysis of these circuits. We are motivated to revisit this topic as these circuits, through their use in general purpose blockchain networks, may be responsible for the security of millions of dollars in cryptocurrency. © 2024 IEEE.

关键词： Blockchain

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Semantic Modeling of Architecture Decision Records to Enable AI-based Analysis 31

Semantic Modeling of Architecture Decision Records to Enable...

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31st IEEE International conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Karetnikov, Aleksei Ehrlinger, Lisa Buchgeher, Georg Geist, Verena Software Competence Center Hagenberg GmbH Hagenberg Austria

ISBN: (纸本)9798350330663

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 makes them easy to use even for software architects with little experience. A promising research direction is the reuse of the recorded architecture knowledge on a cross-project basis. The rapidly growing number of design decisions in different technology stacks, however, makes manual analysis arduous, which is why AI-based methods are required to facilitate efficient identification of relevant knowledge. In this paper, we investigate the suitability of knowledge graphs for analyzing ADRs for providing easy access to domain knowl-edge and enabling the discovery of additional insights. The main challenges are to model the ontology (i.e., the knowledge graph schema) from ADRs and to find a mapping to Wikidata concepts to enrich the knowledge graph with public knowledge. We explain and verify our approach to create a semantic model of existing data sources within a case study, for which we derived over 4,000 pre-filtered open-source ADRs from GitHub, and present initial analysis results. We believe that this approach could impact software engineering practice by assisting software architects in the deeper analysis of current ADR practices and the creation of new ADRs by proposing best practices in specific domains. © 2024 IEEE.

关键词： software architecture

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Teaching UML using a RAG-based LLM

Teaching UML using a RAG-based LLM

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International Joint conference on Neural Networks (IJCNN)

作者： Ardimento, Pasquale Bernardi, Mario Luca Cimitile, Marta Univ Bari Aldo Moro Dept Comp Sci Bari Italy Univ Sannio Dept Engn Benevento Italy UnitelmaSapienza Univ Dept Law & Econ Rome Italy

ISBN: (纸本)9798350359329;9798350359312

Teaching the Unified Modelling Language (UML) is a critical task in the frame of software engineering 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 software engineering 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.

关键词： Deep Learning Generative AI LLMs Computing Education UML software Modelling

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The Metamorphic Lighthouse: Understanding the Input Data Space of Metamorphic Relations 50

The Metamorphic Lighthouse: Understanding the Input Data Spa...

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50th Euromicro conference on software engineering and Advanced Applications

作者： Duque-Torres, Alejandra Pfahl, Dietmar Klammer, Claus Fischer, Stefan Ramler, Rudolf Univ Tartu Inst Comp Sci Tartu Estonia Software Competence Ctr Hagenberg SCCH GmbH Hagenberg Austria

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.

关键词： Metamorphic Testing Metamorphic Relations Test Oracle Pattern Mining Association Rule Mining

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On-Demand Security Requirements Synthesis with Relational Generative Adversarial Networks 23

On-Demand Security Requirements Synthesis with Relational Ge...

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45th IEEE/ACM International conference on software engineering (ICSE)

作者： Koscinski, Viktoria Hashemi, Sara Mirakhorli, Mehdi Rochester Inst Technol Rochester NY USA

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.

关键词： software Security Requirements Requirements engineering Generative Adversarial Networks

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UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services 2

UCTB: An Urban Computing Tool Box for Building Spatiotempora...

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2nd IEEE International conference on software Services engineering (SSE)

作者： Fang, Jiangyi Chen, Liyue Chai, Di Hong, Yayao Xie, Xiuhuai Chen, Longbiao Wang, Leye Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

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.

关键词： spatiotemporal prediction toolbox software service workflow

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