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

D
Docker
月光博客
月光博客
B
Blog RSS Feed
C
Check Point Blog
WordPress大学
WordPress大学
T
Tailwind CSS Blog
GbyAI
GbyAI
H
Help Net Security
Y
Y Combinator Blog
I
InfoQ
雷峰网
雷峰网
阮一峰的网络日志
阮一峰的网络日志
小众软件
小众软件
美团技术团队
博客园 - 三生石上(FineUI控件)
MongoDB | Blog
MongoDB | Blog
Jina AI
Jina AI
A
About on SuperTechFans
G
Google Developers Blog
爱范儿
爱范儿
F
Fortinet All Blogs
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
U
Unit 42
人人都是产品经理
人人都是产品经理

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
An Energy-aware Mutation Testing Framework for EAST-ADL A...
Raluca Marinescu, Predrag Filipovikj, Eduard Paul Enoiu, Jonatan · 2018-02-04 · via cs.SE updates on arXiv.org

Early design artifacts of embedded systems, such as architectural models, represent convenient abstractions for reasoning about a system's structure and functionality. One such example is the Electronic Architecture and Software Tools-Architecture Description Language (EAST-ADL), a domain-specific architectural language that targets the automotive industry. EAST-ADL is used to represent both hardware and software elements, as well as related extra-functional information (e.g., timing properties, triggering information, resource consumption). Testing architectural models is an important activity in engineering large-scale industrial systems, which sparks a growing research interest. The main contributions of this paper are: (i) an approach for creating energy-related mutants for EAST-ADL architectural models, (ii) a method for overcoming the equivalent mutant problem (i.e., the problem of finding a test case which can distinguish the observable behavior of a mutant from the original one), (iii) a test generation approach based on UPPAAL Statistical Model Checker (SMC), and (iv) a test selection criteria based on mutation analysis using our MATS tool.