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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
Annotating Control-Flow Graphs for Formalized Test Covera...
Sean Kauffman, Carlos Moreno, Sebastian Fischmeister · 2024-07-05 · via cs.SE updates on arXiv.org

Control flow coverage criteria are an important part of the process of qualifying embedded software for safety-critical systems. Criteria such as modified condition/decision coverage (MC/DC) as defined by DO-178B are used by regulators to judge the adequacy of testing and by QA engineers to design tests when full path coverage is impossible. Despite their importance, these coverage criteria are often misunderstood. One problem is that their definitions are typically written in natural language specification documents, making them imprecise. Other works have proposed formal definitions using binary predicate logic, but these definitions are difficult to apply to the analysis of real programs. Control-Flow Graphs (CFGs) are the most common model for analyzing program logic in compilers, and seem to be a good fit for defining and analyzing coverage criteria. However, CFGs discard the explicit concept of a decision, making their use for this task seem impossible. In this paper, we show how to annotate a CFG with decision information inferred from the graph itself. We call this annotated model a Control-Flow Decision Graph (CFDG) and we use it to formally define several common coverage criteria. We have implemented our algorithms in a tool which we show can be applied to automatically annotate CFGs output from popular compilers.