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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
Scalable CI/CD for Legacy Modernization: An Industrial Ex...
Kuniaki Kudo, Sherine Devi · 2025-10-20 · via cs.SE updates on arXiv.org

We have developed a Scalable CI/CD Pipeline to address internal challenges related to Japan 2025 cliff problem, a critical issue where the mass end of service life of legacy core IT systems threatens to significantly increase the maintenance cost and black box nature of these system also leads to difficult update moreover replace, which leads to lack of progress in Digital Transformation (DX). If not addressed, Japan could potentially lose up to 12 trillion yen per year after 2025, which is 3 times more than the cost in previous years. Asahi also faced the same internal challenges regarding legacy system, where manual maintenance workflows and limited QA environment have left critical systems outdated and difficult to update. Middleware and OS version have remained unchanged for years, leading to now its nearing end of service life which require huge maintenance cost and effort to continue its operation. To address this problem, we have developed and implemented a Scalable CI/CD Pipeline where isolated development environments can be created and deleted dynamically and is scalable as needed. This Scalable CI/CD Pipeline incorporate GitHub for source code control and branching, Jenkins for pipeline automation, Amazon Web Services for scalable environment, and Docker for environment containerization. This paper presents the design and architecture of the Scalable CI/CD Pipeline, with the implementation along with some use cases. Through Scalable CI/CD, developers can freely and safely test maintenance procedures and do experiments with new technology in their own environment, reducing maintenance cost and drive Digital Transformation (DX). key words: 2025 Japan Cliff, Scalable CI/CD, DevOps, Legacy IT Modernization.