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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
The Role of the Retrospective Meetings in Detecting, Refa...
Carlos Dantas, Tiago Massoni, Camila Sarmento, Rayana Rocha, Dan · 2025-02-26 · via cs.SE updates on arXiv.org

Retrospective meetings play a vital role in agile development by facilitating team reflection on past work to enhance effectiveness. These meetings address various social aspects, including team dynamics, individual performance, processes, and technologies, ultimately leading to actions for improvement. Despite their importance, limited research has explored how these meetings handle forms of social debt, particularly Community Smells -- recurring dysfunctional patterns in team dynamics, such as poor communication or isolated work practices. This study seeks to understand how retrospective meetings address a few core Community Smells, examining whether these meetings help identify smells, make it possible to formulate refactoring strategies, support the monitoring of refactoring actions, and contribute to preventing the most prominent Community Smells. We conducted semi-structured interviews with 15 practitioners from diverse organizations who regularly participate in retrospective meetings. The interviewees shared their experiences with retrospectives, the challenges discussed, and subsequent improvement actions. The study focused on the four most cited Community Smells in the literature -- Lone Wolf, Organizational Silo, Radio Silence, and Black Cloud. Data was analyzed iteratively using a priori coding to examine Community Smells and inductive open coding inspired by Grounded Theory. The findings indicate that retrospective meetings indeed enable the identification of core Community Smells. However, while strategies for refactoring are often formulated, their implementation and monitoring remain inconsistent. Additionally, an emphasis on positive aspects during these meetings may help in preventing Community Smells. This study offers valuable insights to practitioners and researchers, highlighting the importance of addressing social debt in software development within agile practices.