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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
A Qualitative Study of Architectural Design Issues in DevOps
Mojtaba Shahin, Ali Rezaei Nasab, Muhammad Ali Babar · 2021-08-15 · via cs.SE updates on arXiv.org

Software architecture is critical in succeeding with DevOps. However, designing software architectures that enable and support DevOps (DevOps-driven software architectures) is a challenge for organizations. We assert that one of the essential steps towards characterizing DevOps-driven architectures is to understand architectural design issues raised in DevOps. At the same time, some of the architectural issues that emerge in the DevOps context (and their corresponding architectural practices or tactics) may stem from the context (i.e., domain) and characteristics of software organizations. To this end, we conducted a mixed-methods study that consists of a qualitative case study of two teams in a company during their DevOps transformation and a content analysis of Stack Overflow and DevOps Stack Exchange posts to understand architectural design issues in DevOps. Our study found eight specific and contextual architectural design issues faced by the two teams and classified architectural design issues discussed in Stack Overflow and DevOps Stack Exchange into 11 groups. Our aggregated results reveal that the main characteristics of DevOps-driven architectures are: being loosely coupled and prioritizing deployability, testability, supportability, and modifiability over other quality attributes. Finally, we discuss some concrete implications for research and practice.