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

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
爱范儿
爱范儿
WordPress大学
WordPress大学
博客园 - 三生石上(FineUI控件)
J
Java Code Geeks
Vercel News
Vercel News
aimingoo的专栏
aimingoo的专栏
T
Tailwind CSS Blog
罗磊的独立博客
B
Blog
博客园_首页
A
About on SuperTechFans
有赞技术团队
有赞技术团队
V
V2EX
U
Unit 42
I
InfoQ
IT之家
IT之家
博客园 - 司徒正美
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Stack Overflow Blog
Stack Overflow Blog
The Cloudflare Blog
H
Help Net Security

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
Learn More, Pay Less! Lessons Learned from Applying the W...
Zahra Shakeri Hossein Abad, Shane D. V. Sims, Abdullah Cheema, M · 2017-07-18 · via cs.SE updates on arXiv.org

Mobile apps have exploded in popularity, encouraging developers to provide content to the massive user base of the main app stores. Although there exist automated techniques that can classify user comments into various topics with high levels of precision, recent studies have shown that the top apps in the app stores do not have customer ratings that directly correlate with the app's success. This implies that no single requirements elicitation technique can cover the full depth required to produce a successful product and that applying alternative requirements gathering techniques can lead to success when these two are combined. Since user involvement has been found to be the most impactful contribution to project success, in this paper we will explore how the Wizard of Oz (WOz) technique and user reviews available in Google Play, can be integrated to produce a product that meets the demand of more stakeholders than either method alone. To compare the role of early interactive requirements specification and app reviews, we conducted two studies (i) a case study analysis on 13 mobile app development teams who used very early stages Requirements Engineering (RE) by applying WOz, and (ii) a study analyzing 40 (70, 592 reviews) similar mobile apps on Google Play. The results of both studies show that while each of WOz and app review analysis techniques can be applied to capture specific types of requirements, an integrated process including both methods would eliminate the communication gap between users and developers at early stages of the development process and mitigates the risk of requirements change in later stages.