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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
An Empirical Study of Complexity, Heterogeneity, and Comp...
[Submitted on 24 Jul 2025 (v1), last revised 3 Aug 2026 (this ve · 2025-07-24 · via cs.SE updates on arXiv.org

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Abstract:Continuous Integration (CI) has become a core practice in modern software engineering, enabling rapid and collaborative software delivery. GitHub Actions (GHA) has become a leading CI platform due to its tight GitHub integration and growing ecosystem of reusable workflows. Despite extensive documentation and best practices, there is limited empirical understanding of how real-world GHA workflows align with recommended guidelines. This study analyzes the structure, complexity, heterogeneity, and compliance of GHA workflows across Java, Python, and C++ repositories. We (a) quantify workflow complexity, (b) identify recurring and diverse structural patterns, (c) evaluate compliance with best practices, and (d) compare workflow design across languages. GHA workflows are generally small, shallow, and heavily dependent on external actions, with limited sequence-level standardization despite recurring intent-level patterns. A common pipeline prefix appears in 39.5% of workflows, but workflow sequences are highly heterogeneous, with only one exceeding the 5% global frequency threshold. We observe that Java has the lowest explicit test adoption, Python follows canonical templates but shows weaker security practices, and C++ workflows are larger and more structurally diverse. Compliance gaps are widespread, especially in permissions, timeout configuration, and SHA pinning, while reusable workflows remain rare. Build-without-test patterns suggest that workflow design is driven more by ecosystem conventions and platform defaults than by best practices. This indicates that better defaults and tooling could improve workflow security, modularity, and maintainability, while researchers should account for language- and repository-level factors when analyzing CI systems. Overall, this work provides a reproducible empirical baseline for studying and improving GHA workflow design in open-source ecosystems.

Submission history

From: Taher A. Ghaleb [view email]
[v1] Thu, 24 Jul 2025 03:26:38 UTC (120 KB)
[v2] Mon, 3 Aug 2026 02:19:45 UTC (405 KB)