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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
Choosing the Right Git Workflow: A Comparative Analysis o...
Pedro Lopes, Paola Accioly, Paulo Borba, Vitor Menezes · 2025-07-12 · via cs.SE updates on arXiv.org

Git has become one of the most widely used version control systems today. Among its distinguishing features, its ability to easily and quickly create branches stands out, allowing teams to customize their workflows. In this context, various formats of collaborative development workflows using Git have emerged and gained popularity among software engineers. We can categorize such workflows into two main types: branch-based workflows and trunk-based workflows. Branch-based workflows typically define a set of remote branches with well-defined objectives, such as feature branches, a branch for feature integration, and a main branch. The goal is to migrate changes from the most isolated branch to the main one shared by all as the code matures. In this category, GitFlow stands out as the most popular example. In contrast, trunk-based workflows have a single remote branch where developers integrate their changes directly. In this range of options, choosing a workflow that maximizes team productivity while promoting software quality becomes a non-trivial task. Despite discussions on forums, social networks, and blogs, few scientific articles have explored this topic. In this work, we provide evidence on how Brazilian developers work with Git workflows and what factors favor or hinder the use of each model. To this end, we conducted semi-structured interviews and a survey with software developers. Our results indicate that trunk-based development favors fast-paced projects with experienced and smaller teams, while branch-based development suits less experienced and larger teams better, despite posing management challenges.