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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
Prioritising Server Side Reachability via Inter-process C...
Maarten Vandercammen, Laurent Christophe, Dario Di Nucci, Wolfga · 2020-10-30 · via cs.SE updates on arXiv.org

Context: Most approaches to automated white-box testing consider the client side and the server side of a web application in isolation from each other. Such testers lack a whole-program perspective on the web application under test. Inquiry: We hypothesise that an additional whole-program perspective would enable the tester to discover which server side errors can be triggered by an actual end user accessing the application through the client, and which ones can only be triggered in hypothetical scenarios. Approach: In this paper, we explore the idea of employing such a whole-program perspective in testing. To this end, we develop , a novel concolic tester which operates on full-stack JavaScript web applications, where both the client and the server side are JavaScript processes communicating via asynchronous messages -- as enabled by the WebSocket or Socket.IO-libraries. Knowledge: We find that the whole-program perspective enables discerning high-priority errors, which are reachable from a particular client, from low-priority errors, which are not accessible through the tested client. Another benefit of the perspective is that it allows the automated tester to construct practical, step-by-step scenarios for triggering server side errors from the end user's perspective. Grounding: We apply on a collection of web applications to evaluate how effective testing is in distinguishing between high- and low-priority errors. The results show that correctly classifies the majority of server errors. Importance: This paper demonstrates the feasibility of testing as a novel approach for automatically testing web applications. Classifying errors as being of high or low importance aids developers in prioritising bugs that might be encountered by users, and postponing the diagnosis of bugs that are less easily reached.