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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
Where Are The Gaps? A Systematic Mapping Study of Infrast...
Akond Rahman, Rezvan Mahdavi-Hezaveh, Laurie Williams · 2018-07-13 · via cs.SE updates on arXiv.org

Context:Infrastructure as code (IaC) is the practice to automatically configure system dependencies and to provision local and remote instances. Practitioners consider IaC as a fundamental pillar to implement DevOps practices, which helps them to rapidly deliver software and services to end-users. Information technology (IT) organizations, such as Github, Mozilla, Facebook, Google and Netflix have adopted IaC. A systematic mapping study on existing IaC research can help researchers to identify potential research areas related to IaC, for example, the areas of defects and security flaws that may occur in IaC scripts. Objective: The objective of this paper is to help researchers identify research areas related to infrastructure as code (IaC) by conducting a systematic mapping study of IaC-related research. Methodology: We conduct our research study by searching six scholar databases. We collect a set of 33,887 publications by using seven search strings. By systematically applying inclusion and exclusion criteria, we identify 31 publications related to IaC. We identify topics addressed in these publications by applying qualitative analysis. Results: We identify four topics studied in IaC-related publications: (i) framework/tool for infrastructure as code; (ii) use of infrastructure as code; (iii) empirical study related to infrastructure as code; and (iv) testing in infrastructure as code. According to our analysis, 52% of the studied 31 publications propose a framework or tool to implement the practice of IaC or extend the functionality of an existing IaC tool. Conclusion: As defects and security flaws can have serious consequences for the deployment and development environments in DevOps, along with other topics, we observe the need for research studies that will study defects and security flaws for IaC.