惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Jina AI
Jina AI
博客园 - Franky
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
阮一峰的网络日志
阮一峰的网络日志
量子位
雷峰网
雷峰网
宝玉的分享
宝玉的分享
V
Visual Studio Blog
博客园_首页
小众软件
小众软件
The Cloudflare Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
大猫的无限游戏
大猫的无限游戏
博客园 - 聂微东
S
SegmentFault 最新的问题
博客园 - 【当耐特】
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 叶小钗
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Building Confidence in Scientific Computing Software Via ...
Spencer Smith, Mojdeh Sayari Nejad, Alan Wassyng · 2019-12-31 · via cs.SE updates on arXiv.org

Assurance cases provide an organized and explicit argument for correctness. They can dramatically improve the certification of Scientific Computing Software (SCS). Assurance cases have already been effectively used for safety cases for real time systems. Their advantages for SCS include engaging domain experts, producing only necessary documentation, and providing evidence that can be verified/replicated. This paper illustrates assurance cases for SCS through the correctness case for 3dfim+, an existing Medical Imaging Application (MIA) for analyzing activity in the brain. This example was partly chosen because of recent concerns about the validity of fMRI (Functional Magnetic Resonance Imaging) studies. The example justifies the value of assurance cases for SCS, since the existing documentation is shown to have ambiguities and omissions, such as an incompletely defined ranking function and missing details on the coordinate system. A serious concern for 3dfim+ is identified: running the software does not produce any warning about the necessity of using data that matches the parametric statistical model employed for the correlation calculations. Raising the bar for SCS in general, and MIA in particular, is both feasible and necessary - when software impacts safety, an assurance case methodology (or an equivalently rigorous confidence building methodology) should be employed.