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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
A Large-Scale Empirical Study of COVID-19 Contact Tracing...
Sifat Ishmam Parisa, Md Awsaf Alam Anindya, Anindya Iqbal, Gias · 2024-04-28 · via cs.SE updates on arXiv.org

Since the beginning of 2020, the novel coronavirus has begun to sweep across the globe. Given the prevalence of smartphones everywhere, many countries across continents also developed COVID-19 contract tracing apps that users can install to get a warning of potential contacts with infected people. Unlike regular apps that undergo detailed requirement analysis, carefully designed development, rigorous testing, contact tracing apps were deployed after rapid development. Therefore such apps may not reach expectations for all end users. Users share their opinions and experience of the usage of the apps in the app store. This paper aims to understand the types of topics users discuss in the reviews of the COVID-19 contact tracing apps across the continents by analyzing the app reviews. We collected all the reviews of 35 COVID-19 contact tracing apps developed by 34 countries across the globe. We group the app reviews into the following geographical regions: Asia, Europe, North America, Latin America, Africa, Middle East, and Australasia (Australia and NZ). We run topic modeling on the app reviews of each region. We analyze the produced topics and their evolution over time by categorizing them into hierarchies and computing the ratings of reviews related to the topics. While privacy could be a concern with such apps, we only find privacy-related topics in Australasia, North America, and Middle East. Topics related to usability and performance of the apps are prevalent across all regions. Users frequently complained about the lack of features, user interface and the negative impact of such apps on their mobile batteries. Still, we also find that many users praised the apps because they helped them stay aware of the potential danger of getting infected. The finding of this study is expected to help app developers utilize their resources to address the reported issues in a prioritized way.