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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
What Is the Best Way For Developers to Learn New Software...
2017-04-01 · via cs.SE updates on arXiv.org

The better developers can learn software tools, the faster they can start using them and the more efficiently they can later work with them. Tutorials are supposed to help here. While in the early days of computing, mostly text tutorials were available, nowadays software developers can choose among a huge number of tutorials for almost any popular software tool. However, only little research was conducted to understand how text tutorials differ from other tutorials, which tutorial types are preferred and, especially, which tutorial types yield the best learning experience in terms of efficiency and effectiveness, especially for programmers. To evaluate these questions, we converted an existing video tutorial for a novel software tool into a content-equivalent text tutorial. We then conducted an experiment in three groups where 42 undergraduate students from a software engineering course were commissioned to operate the software tool after using a tutorial: the first group was provided only with the video tutorial, the second group only with the text tutorial and the third group with both. In this context, the differences in terms of efficiency were almost negligible: We could observe that participants using only the text tutorial completed the tutorial faster than the participants with the video tutorial. However, the participants using only the video tutorial applied the learned content faster, achieving roughly the same bottom line performance. We also found that if both tutorial types are offered, participants prefer video tutorials for learning new content but text tutorials for looking up "missed" information. We mainly gathered our data through questionnaires and screen recordings and analyzed it with suitable statistical hypotheses tests. The data is available at [12]. Since producing tutorials requires effort, knowing with which type of tutorial learnability can be increased to which extent has an immense practical relevance. We conclude that in contexts similar to ours, while it would be ideal if software tool makers would offer both tutorial types, it seems more efficient to produce only text tutorials instead of a passive video tutorial - provided you manage to motivate your learners to use them.