In contemporary programming languages that lack automatic memory management, such as C/C++, ensuring memory safety remains an unresolved practical challenge. Applications developed in these languages often exhibit var...
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ISBN:
(纸本)9783031664557;9783031664564
In contemporary programming languages that lack automatic memory management, such as C/C++, ensuring memory safety remains an unresolved practical challenge. Applications developed in these languages often exhibit various safety vulnerabilities. While numerous solutions have been proposed by both academia and industry, some of which have gained widespread adoption, they commonly present limitations such as the requirement for specialized hardware support, significant runtime or memory overhead, or a limited scope of problem coverage. In this paper, we present an efficient, software-based memory safety violation mitigation scheme based on intermediate pointers and meta-data embedding for the Intel x86-64 platform. The fundamental idea is to insert intermediate pointers to every pointer that points to heap memory and embed tags in the unused bits of the intermediate pointers. By inserting checks on these intermediate pointers, potential memory safety violations can be mitigated. Based on this scheme, we implement SafePtrX, a mitigation solution for heap memory safety with enhanced security properties and improved performance compared to existing methods. We also demonstrate the feasibility of SafePtrX by using publicly disclosed vulnerabilities.
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the softw...
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ISBN:
(纸本)9783031808883;9783031808890
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their labor-intensive nature. Large Language Models (LLMs) present a promising solution to these challenges. However, their application generally demands multiple rounds of human-driven prompt engineering, which limits their practical deployment, particularly for industrial end-users who need reliable and efficient results. In this paper, we propose GoNoGo, an LLM agent system designed to streamline automotive software deployment while meeting both functional requirements and practical industrial constraints. Unlike previous systems, GoNoGo is specifically tailored to address domain-specific and risk-sensitive systems. We evaluate GoNoGo's performance across different task difficulties using zero-shot and few-shot examples taken from industrial practice. Our results show that GoNoGo achieves a 100% success rate for tasks up to Level 2 difficulty with 3-shot examples, and maintains high performance even for more complex tasks. We find that GoNoGo effectively automates decision-making for simpler tasks, significantly reducing the need for manual intervention. In summary, GoNoGo represents an efficient and user-friendly LLM-based solution currently employed in our industrial partner's company to assist with software release decision-making, supporting more informed and timely decisions in the release process for risk-sensitive vehicle systems.
Early defect detection and prediction are essential in softwareengineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on ...
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The design and analysis of security in distributed computing systems raises numerous questions on the tools available for modeling and verification. Particularly, it is difficult to ensure the correctness when using d...
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The proceedings contain 29 papers. The special focus in this conference is on Requirements engineering: Foundation for software Quality. The topics include: Towards Ethics-Driven Requirements engineering: Integrating ...
ISBN:
(纸本)9783031885303
The proceedings contain 29 papers. The special focus in this conference is on Requirements engineering: Foundation for software Quality. The topics include: Towards Ethics-Driven Requirements engineering: Integrating Critical systems Heuristics and Ethical Guidelines for Autonomous Vehicles;refining and Validating Change Requests from a Crowd to Derive Requirements;do Users’ Explainability Needs in software Change with Mood?;exploring and Characterizing Ad-Hoc Requirements - A Case Study at a Large-Scale systems Provider;feReRe: Feedback Requirements Relation Using Large Language Models;how Does Users’ App Knowledge Influence the Preferred Level of Detail and Format of software Explanations?;How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?;an Interactive Tool for Goal Model Construction Using a Knowledge Graph;Generating Domain Models with LLMs Using Instruction Tuning: A Research Preview;A Systematic Literature Review of KAOS Extensions;LACE-HC: A Lightweight Attention-Based Classifier for Efficient Hierarchical Classification of software Requirements;requirements Representations in Machine Learning-Based Automotive Perception systems Development for Multi-party Collaboration;automatic Prompt engineering: The Case of Requirements Classification;exploring Generative Pretrained Transformers to Support Sustainability Effect Identification - A Research Preview;prompt Me: Intelligent software Agent for Requirements engineering - A Vision Paper;detecting Redundancies Between User Stories with Graphs and Large Language Models;Leveraging Requirements Elicitation through software Requirement Patterns and LLMs;ReqRAG: Enhancing software Release Management through Retrieval-Augmented LLMs: An Industrial Study;the Potential of Citizen Platforms for Requirements engineering of Large Socio-Technical softwaresystems;end-User Requirements Modelling: An Experience Report from Digital Agriculture;requirements Elicitation Workshops Using the Six Thinking Hat
Multi-Criteria Decision-Making (MCDM) plays a pivotal role in the field of computer science and softwareengineering, offering a systematic approach to decision-making processes. The integration of various MCDM method...
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Semiotics is the discipline that studies the signs and the cognitive process of meaning-making. As part of semiotics studies, the idea of semiospheres has been formulated, representing spheres of meaning that do not e...
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The electronic safety and arming device test system is developed to ensure the reliability of weapon systems. In view of the low test efficiency and high requirements for testers in manual testing during the current t...
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A collective adaptive system is made up of cooperating entities that can adjust in real time to evolving, open environments and shifting requirements. To ensure such a system meets its intended goals, rigorous enginee...
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ISBN:
(纸本)9783031751066;9783031751073
A collective adaptive system is made up of cooperating entities that can adjust in real time to evolving, open environments and shifting requirements. To ensure such a system meets its intended goals, rigorous engineering must employ suitable methods and tools. This introduction offers a short overview of the 5(th) edition of the track "Rigorous engineering of Collective Adaptive systems" and briefly presents the 20 scientific contributions, organised into seven thematic sections. Large Ensembles and Collective Dynamics, Knowledge, Consciousness and Emergence, Automated Reasoning for Better Interaction, Modelling and engineering Collective Adaptive systems, Analysing Collective Adaptive systems.
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