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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
Details of an Automotive Sub-System: Saab Instrument Clus...
Ramin Etemaadi, Kenneth Lind, Rogardt Heldal, Michel R. V. Chaud · 2013-06-03 · via cs.SE updates on arXiv.org

The goal of this technical report is to give the details of a real world existing sub-system in the automotive industry. It is produced to be used for reproduction of the same experiment if other researchers are interested in. Hence, it would be possible to compare the results of our published studies with the results of similar tools. The data is collected for the purpose of applying metaheuristic optimization approaches. The case study based on these data shows that metaheuristic optimization approaches can find efficient solutions for multiple quality attributes while fulfilling given constraints. The case study was conducted at Saab Automobile AB in order to evaluate the AQOSA framework in an industrial context. AQOSA (Automated Quality-driven Optimization of Software Architecture) is our architecture optimization framework that supports multiple quality attributes including response time, processor utilization, bus utilization, safety and cost. To enable validation of the results we selected an existing realization for the Saab 9-5 Instrument Cluster Module ECU (Electronic Control Unit) and the surrounding sub-systems. The goal of the case study is to find a better solution than the current realization while fulfilling the requirements and constraints. The results of the case study and the details of AQOSA framework is reported in a paper from the authors in Journal of Systems and Software, Special Issue on Quality Optimization of Software Architecture and Design Specifications.