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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
BARR-C:2018 and MISRA C:2012: Synergy Between the Two Mos...
Roberto Bagnara, Michael Barr, Patricia M. Hill · 2020-03-16 · via cs.SE updates on arXiv.org

The Barr Group's Embedded C Coding Standard (BARR-C:2018, which originates from the 2009 Netrino's Embedded C Coding Standard) is, for coding standards used by the embedded system industry, second only in popularity to MISRA C. However, the choice between MISRA C:2012 and BARR-C:2018 needs not be a hard decision since they are complementary in two quite different ways. On the one hand, BARR-C:2018 has removed all the incompatibilities with respect to MISRA C:2012 that were present in the previous edition (BARR-C:2013). As a result, disregarding programming style, BARR-C:2018 defines a subset of C that, while preventing a significant number of programming errors, is larger than the one defined by MISRA C:2012. On the other hand, concerning programming style, whereas MISRA C leaves this to individual organizations, BARR-C:2018 defines a programming style aimed primarily at minimizing programming errors. As a result, BARR-C:2018 can be seen as a first, dramatically useful step to C language subsetting that is suitable for all kinds of projects; critical projects can then evolve toward MISRA C:2012 compliance smoothly while maintaining the BARR-C programming style. In this paper, we introduce BARR-C:2018, we describe its relationship with MISRA C:2012, and we discuss the parallel and serial adoption of the two coding standards.