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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
SafeRESTScript: Statically Checking REST API Consumers
Nuno Burnay, Antónia Lopes, Vasco T. Vasconcelos · 2020-07-16 · via cs.SE updates on arXiv.org

Consumption of REST services has become a popular means of invoking code provided by third parties, particularly in web applications. Nowadays programmers of web applications can choose TypeScript over JavaScript to benefit from static type checking that enables validating calls to local functions or to those provided by libraries. Errors in calls to REST services, however, can only be found at run-time. In this paper, we present SafeRESTScript (SRS, for short) a language that extends the support of static analysis to calls to REST services, with the ability to statically find common errors such as missing or invalid data in REST calls and misuse of the results from such calls. SafeRESTScript features a syntax similar to JavaScript and is equipped with (i) a rich collection of types (including objects, arrays and refinement types)and (ii) primitives to natively support REST calls that are statically validated against specifications of the corresponding APIs. Specifications are written in HeadREST, a language that also features refinement types and supports the description of semantic aspects of REST APIs in a style reminiscent of Hoare triples. We present SafeRESTScript and its validation system, based on a general-purpose verification tool (Boogie). The evaluation of SafeRESTScript and of the prototype implementations for its validator, available in the form of an Eclipse plugin, is also discussed.