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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
Quality Control, Testing and Deployment Results in NIF ICCS
John P. Woodruff, Drew D. Casavant, Barry D. Cline, Michael R. G · 2001-11-07 · via cs.SE updates on arXiv.org

The strategy used to develop the NIF Integrated Computer Control System (ICCS) calls for incremental cycles of construction and formal test to deliver a total of 1 million lines of code. Each incremental release takes four to six months to implement specific functionality and culminates when offline tests conducted in the ICCS Integration and Test Facility verify functional, performance, and interface requirements. Tests are then repeated on line to confirm integrated operation in dedicated laser laboratories or ultimately in the NIF. Test incidents along with other change requests are recorded and tracked to closure by the software change control board (SCCB). Annual independent audits advise management on software process improvements. Extensive experience has been gained by integrating controls in the prototype laser preamplifier laboratory. The control system installed in the preamplifier lab contains five of the ten planned supervisory subsystems and seven of sixteen planned front-end processors (FEPs). Beam alignment, timing, diagnosis and laser pulse amplification up to 20 joules was tested through an automated series of shots. Other laboratories have provided integrated testing of six additional FEPs. Process measurements including earned-value, product size, and defect densities provide software project controls and generate confidence that the control system will be successfully deployed.