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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
StartFlow: From Method Conception to Multi-Perspective Ev...
Guilherme Corredato Guerino, João Pedro de Souza Olivo Tardivo, · 2026-05-12 · via cs.SE updates on arXiv.org

Context. Software startups face significant challenges in building minimum viable products, particularly in the early stages, when resources are limited and expertise in user experience is scarce. Objective. Introduce StartFlow, a structured method that helps non-specialized professionals create MVP prototypes using the wireflow technique, a combination of wireframes and user flows. StartFlow consists of three steps: (i) organizing features; (ii) building wireflows; and (iii) verifying and refining them based on usability heuristics. Method. To assess the method Startflow, we first conducted a focus group with researchers in Software Engineering, Human-Computer Interaction, and Software Startups. Afterward, we conducted a proof-of-concept study, which consisted of an experiment and a heuristic evaluation with experts. Results. The qualitative analysis of the focus group revealed that participants found the method straightforward, flexible, and helpful in structuring user flows and identifying visual components. However, they also pointed out the need to improve its presentation, clarify its iterative nature, and strengthen its connection to broader UX principles. The results of the proof-of-concept indicate that participants who used StartFlow created clearer prototypes, adhered to the proposed user stories and business rules, and presented fewer usability defects. Furthermore, the method was well evaluated for its ease of use and intended future adoption. Conclusion. The study reinforces the potential of StartFlow as an accessible tool to support user-centered development in software startups from the earliest stages of their product development.