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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
How Do OSS Developers Reuse Architectural Solutions from ...
Musengamana Jean de Dieu, Peng Liang, Mojtaba Shahin · 2024-04-08 · via cs.SE updates on arXiv.org

Developers reuse programming-related knowledge on Q&A sites that functionally matches the programming problems they encounter in their development. Despite extensive research on Q&A sites, being a high-level and important type of development-related knowledge, architectural solutions and their reuse are rarely explored. To fill this gap, we conducted a mixed-methods study that includes a mining study and a survey study. For the mining study, we mined 984 commits and issues from 893 OSS projects on GitHub that explicitly referenced architectural solutions from SO and SWESE. For the survey study, we identified practitioners involved in the reuse of these architectural solutions and surveyed 227 of them to further understand how practitioners reuse architectural solutions from Q&A sites in their OSS development. Our findings: (1) OSS practitioners use architectural solutions from Q&A sites to solve a large variety of architectural problems, wherein Component design issue, Architectural anti-pattern, and Security issue are dominant; (2) Seven categories of architectural solutions from Q&A sites have been reused to solve those problems, among which Architectural refactoring, Use of frameworks, and Architectural tactic are the three most reused architectural solutions; (3) OSS developers often rely on ad hoc ways (e.g., informal, improvised, or unstructured approaches) to incorporate architectural solutions from SO, drawing on personal experience and intuition rather than standardized or systematic practices; (4) Using architectural solutions from SO comes with a variety of challenges, e.g., OSS practitioners complain that they need to spend significant time to adapt such architectural solutions to address design concerns raised in their OSS development, and it is challenging to use architectural solutions that are not tailored to the design context of their OSS projects.