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

Martin Fowler
Martin Fowler
Y
Y Combinator Blog
M
MIT News - Artificial intelligence
The Cloudflare Blog
WordPress大学
WordPress大学
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 司徒正美
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
C
Check Point Blog
D
DataBreaches.Net
T
The Blog of Author Tim Ferriss
V
V2EX
F
Fortinet All Blogs
B
Blog
大猫的无限游戏
大猫的无限游戏
N
Netflix TechBlog - Medium
B
Blog RSS Feed
A
About on SuperTechFans
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

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
Automated Validation of Insurance Applications against Ca...
Advaita Datar, Amey Zare, Asia A, R Venkatesh, Shrawan Kumar, Ul · 2022-09-08 · via cs.SE updates on arXiv.org

Insurance companies rely on their Legacy Insurance System (LIS) to govern day-to-day operations. These LIS operate as per the companys business rules that are formally specified in Calculation Specification (CS) sheets. To meet ever-changing business demands, insurance companies are increasingly transforming their outdated LIS to modern Policy Administration Systems (PAS). Quality Assurance (QA) of such PAS involves manual validation of calculations implementation against the corresponding CS sheets from the LIS. This manual QA approach is effort-intensive and error-prone, which may fail to detect inconsistencies in PAS implementations and ultimately result in monetary loss. To address this challenge, we propose a novel low-code no-code technique to automatically validate PAS implementation against CS sheets. Our technique has been evaluated on a digital transformation project of a large insurance company on 12 real-world calculations through 254 policies. The evaluation resulted in effort savings of approximately 92 percent against the conventional manual validation approach.