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

P
Proofpoint News Feed
博客园_首页
WordPress大学
WordPress大学
大猫的无限游戏
大猫的无限游戏
有赞技术团队
有赞技术团队
阮一峰的网络日志
阮一峰的网络日志
Hugging Face - Blog
Hugging Face - Blog
博客园 - 【当耐特】
酷 壳 – CoolShell
酷 壳 – CoolShell
Y
Y Combinator Blog
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
T
The Blog of Author Tim Ferriss
云风的 BLOG
云风的 BLOG
博客园 - 司徒正美
Engineering at Meta
Engineering at Meta
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
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
N
Netflix TechBlog - Medium
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
G
Google Developers Blog
Last Week in AI
Last Week in AI

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
SAT-Based Extraction of Behavioural Models for Java Libra...
Larisa Safina, Simon Bliudze · 2022-05-31 · via cs.SE updates on arXiv.org

Behavioural models are a valuable tool for software verification, testing, monitoring, publishing etc. However, they are rarely provided by the software developers and have to be extracted either from the source or from the compiled code. In the context of Java programs, a number of approaches exist for building behavioural models. Most of these approaches rely on the analysis of the compiled bytecode. Instead, we are looking to extract behavioural models in the form of Finite State Machines (FSMs) from the Java source code to ensure that the obtained FSMs can be easily understood by the software developers and, if necessary, updated or integrated into the original source code, e.g. in the form of annotations. Modern software systems are huge, rely on external libraries and interact with their environment. Hence, extracting useful behavioural models requires abstraction. In this paper, we present an initial approach to this problem by focusing on the extraction of FSMs modelling library APIs. We focus on the analysis of Java code involving the use of collections. To this end, we encode the operational semantics of collection operations using patterns of Boolean predicates. These patterns are instantiated based on the analysis of the source code of API implementation methods to form an encoding of the possible FSM transitions. A SAT solver is then used to determine the enabledness conditions (guards) of these transitions.