惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

阮一峰的网络日志
阮一峰的网络日志
博客园 - 司徒正美
D
DataBreaches.Net
宝玉的分享
宝玉的分享
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 【当耐特】
人人都是产品经理
人人都是产品经理
博客园 - Franky
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
IT之家
IT之家
博客园 - 三生石上(FineUI控件)
J
Java Code Geeks
腾讯CDC
博客园_首页
The Cloudflare Blog
S
SegmentFault 最新的问题
C
Check Point Blog
美团技术团队
爱范儿
爱范儿
大猫的无限游戏
大猫的无限游戏
Hugging Face - Blog
Hugging Face - Blog
T
The Blog of Author Tim Ferriss
A
About on SuperTechFans
Blog — PlanetScale
Blog — PlanetScale

cs.SE updates on arXiv.org

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
Characterizing Virtual Reality Software Testing
Dhia Elhaq Rzig, Nafees Iqbal, Isabella Attisano, Xue Qin, Foyzu · 2022-11-04 · via cs.SE updates on arXiv.org

Virtual Reality (VR) is an emerging technique that provides a unique real-time experience for users. VR technologies have provided revolutionary user experiences in various scenarios (e.g., training, education, product/architecture design, gaming, remote conference/tour, etc.). However, testing VR applications is challenging due to their nature which necessitates physical interactivity, and their reliance on hardware systems. Despite the recent advancements in VR technology and its usage scenarios, we still know little about VR application testing. To fill up this knowledge gap, we performed an empirical study on 97 open-source VR applications including 28 industrial projects. Our analysis identified that 74.2% of the VR projects evaluated did not have any tests, and for the VR projects that did, the median functional-method to test-method ratio was low in comparison to other project categories. Moreover, we uncovered tool support issues concerning the measurement of VR code coverage, and the code coverage and assertion density results we were able to generate were also relatively low, as they respectively had averages of 15.63% and 17.69%. Finally, through manual analysis of 220 test cases from four VR applications and 281 test cases from four non-VR applications, we identified that VR applications require specific categories of test cases to ensure VR application quality attributes. We believe that our findings constitute a call to action for the VR development community to improve testing aspects and provide directions for software engineering researchers to develop advanced techniques for automatic test case generation and test quality analysis for VR applications.