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

Engineering at Meta
Engineering at Meta
J
Java Code Geeks
I
InfoQ
腾讯CDC
Vercel News
Vercel News
IT之家
IT之家
V
Visual Studio Blog
P
Proofpoint News Feed
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
G
Google Developers Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - 叶小钗
有赞技术团队
有赞技术团队
月光博客
月光博客
Martin Fowler
Martin Fowler
量子位
L
LangChain Blog
B
Blog
Last Week in AI
Last Week in AI
博客园 - 司徒正美
Microsoft Security Blog
Microsoft Security Blog
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
A
About on SuperTechFans

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
Novel Strategy Generating Variable-length State Machine T...
Vaclav Rechtberger, Miroslav Bures, Bestoun S. Ahmed, Hynek Schv · 2022-07-25 · via cs.SE updates on arXiv.org

Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths can be automatically generated from the system model to test such systems using a suitable algorithm. This paper presents a strategy that generates test paths and allows to start and end test paths only in defined states of the finite state machine. The strategy also simultaneously supports generating test paths only of length in a given range. For this purpose, alternative system models, test coverage criteria, and a set of algorithms are developed. The strategy is compared with the best alternative based on the reduction of the test set generated by the established N-switch coverage approach on a mix of 171 industrial and artificially generated problem instances. The proposed strategy outperforms the compared variant in a smaller number of test path steps. The extent varies with the used test coverage criterion and preferred test path length range from none to two and half fold difference. Moreover, the proposed technique detected up to 30% more simple artificial defects inserted into experimental SUT models per one test step than the compared alternative technique. The proposed strategy is well applicable in situations where a possible test path starts and ends in a state machine needs to be reflected and, concurrently, the length of the test paths has to be in a defined range.