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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
Behaviour Driven Development: A Systematic Mapping Study
Leonard Peter Binamungu, Salome Maro · 2023-05-09 · via cs.SE updates on arXiv.org

Context: Behaviour Driven Development (BDD) uses scenarios written in semi-structured natural language to express software requirements in a way that can be understood by all stakeholders. The resulting natural language specifications can also be executed to reveal correct and problematic parts of a software. Although BDD was introduced about two decades ago, there is a lack of secondary studies in peer-reviewed scientific literature. Objective: To understand the current state of BDD research by conducting a systematic mapping study that covers studies published from 2006 to 2021. Method: By following the guidelines for conducting systematic mapping studies in software engineering, we sought to answer research questions on types of venues in which BDD papers have been published, research, contributions, studied topics and their evolution, and evaluation methods used in published BDD research. Results: The study identified 166 papers which were mapped. Key results include the following: the dominance of conference papers; scarcity of research with insights from the industry; shortage of philosophical papers on BDD; acute shortage of metrics for measuring various aspects of BDD specifications and the processes for producing BDD specifications; the dominance of studies on using BDD for facilitating various software development endeavours, improving the BDD process and associated artefacts, and applying BDD in different contexts; scarcity of studies on using BDD alongside other software techniques and technologies; increase in diversity of studied BDD topics; and notable use of case studies and experiments to study different BDD aspects. Conclusion: The paper improves our understanding of the state of the art of BDD, and highlights important areas of focus for future BDD research.