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

Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
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
The Cloudflare Blog
U
Unit 42
D
Docker
Hugging Face - Blog
Hugging Face - Blog
博客园 - 聂微东
Recent Announcements
Recent Announcements
GbyAI
GbyAI
T
The Blog of Author Tim Ferriss
Last Week in AI
Last Week in AI
V
Visual Studio Blog
I
InfoQ
Google DeepMind News
Google DeepMind News
小众软件
小众软件
L
LangChain Blog
C
Check Point Blog
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
J
Java Code Geeks
罗磊的独立博客

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
Software Development with Feature Toggles: Practices used...
Rezvan Mahdavi-Hezaveh, Jacob Dremann, Laurie Williams · 2019-07-14 · via cs.SE updates on arXiv.org

Background: Using feature toggles is a technique that allows developers to either turn a feature on or off with a variable in a conditional statement. Feature toggles are increasingly used by software companies to facilitate continuous integration and continuous delivery. However, using feature toggles inappropriately may cause problems which can have a severe impact, such as code complexity, dead code, and system failure. For example, the erroneous repurposing of an old feature toggle caused Knight Capital Group, an American global financial services firm, to go bankrupt due to the implications of the resultant incorrect system behavior. Aim: The goal of this research project is to aid software practitioners in the use of practices to support software development with feature toggles through an empirical study of feature toggle practice usage by practitioners. Method: We conducted a qualitative analysis of 99 artifacts from the grey literature and 10 peer-reviewed papers about feature toggles. We conducted a survey of practitioners from 38 companies. Results: We identified 17 practices in 4 categories: Management practices, Initialization practices, Implementation practices, and Clean-up practices. We observed that all of the survey respondents use a dedicated tool to create and manage feature toggles in their code. Documenting feature toggle's metadata, setting up the default value for feature toggles, and logging the changes made on feature toggles are also frequently-observed practices. Conclusions: The feature toggle development practices discovered and enumerated in this work can help practitioners more effectively use feature toggles. This work can enable future mining of code repositories to automatically identify feature toggle practices.