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

博客园 - 司徒正美
Jina AI
Jina AI
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 三生石上(FineUI控件)
宝玉的分享
宝玉的分享
MyScale Blog
MyScale Blog
I
InfoQ
爱范儿
爱范儿
Microsoft Security Blog
Microsoft Security Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Stack Overflow Blog
Stack Overflow Blog
T
Tailwind CSS Blog
D
DataBreaches.Net
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
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
The Blog of Author Tim Ferriss
B
Blog
阮一峰的网络日志
阮一峰的网络日志
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
月光博客
月光博客
雷峰网
雷峰网
Recent Announcements
Recent Announcements
量子位
B
Blog RSS Feed

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
Programming Internet of Things, Service, and People (IoTS...
Saurabh Chauhan · 2016-07-12 · via cs.SE updates on arXiv.org

Application development for Internet of Things, Service, and People (IoTSP) is challenging because it involves dealing with the heterogeneity that exists both in Physical and Internet worlds. Second, stakeholders involved in the application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment. Given these, a critical challenge is to enable an application development for IoTSP applications with effectively and efficiently from various stakeholders. Several approaches to tackling this challenge have been proposed in the fields of Wireless Sensor Networks (WSN) and Pervasive Computing, regarded as precursors to the modern day of IoTSP. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoTSP. In view of this, we have built upon existing framework and evolved it into a framework for developing IoTSP applications, with substantial additions and enhancements in high-level modeling languages and their integration into the framework, and we present a comparative evaluation results with existing approaches. This provides the IoTSP community for further benchmarking. The evaluation is carried out on real devices exhibiting heterogeneity. Our experimental analysis and results demonstrate that our approach drastically reduces development effort for IoTSP applications compared to existing approaches.