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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
Teaching Design Science as a Method for Effective Researc...
Oscar Pastor, Mmatshuene Anna Segooa, Jose Ignacio Panach · 2024-07-13 · via cs.SE updates on arXiv.org

Applying Design Science Research (DSR) methodology is becoming a popular working resource for most Information Systems (IS) and Software engineering studies. The research and/or practical design problems that must be faced aim to answer the question of how to create or investigate an artifact in a given context. Precisely characterizing both artifact and context is essential for effective research development. While various design science guidelines and frameworks have been created by experts in IS engineering, emerging researchers and postgraduate students still find it challenging to apply this research methodology correctly. There is limited literature and materials that guide and support teaching novice researchers about the types of artifacts that can be developed to address a particular problem and decision-making in DSR. To address this gap in DSR, in this chapter, we explore DSR from an educational perspective, explaining both the concept of DSR and an effective method for teaching it. This chapter includes examples of DSR, a teaching methodology, learning objectives, and recommendations. Moreover, we have created a survey artifact intended to gather data on the experiences of design science users. The goal is to discover insights into contemporary issues and needs for DSR best practices (and, in consequence, evaluate the methodology we aim to teach). Our survey results offer a comprehensive overview of participants' experiences with DSR in the SE and IS Engineering domain, highlighting broad engagement primarily initiated during PhD studies and driven by supervisory guidance and research problems. We finally disclose the artifact to the community so that it can be used by other educators as a preparation when planning to teach DSR in tune with the experiences of their target audience.