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

WordPress大学
WordPress大学
J
Java Code Geeks
Martin Fowler
Martin Fowler
Microsoft Azure Blog
Microsoft Azure Blog
月光博客
月光博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
有赞技术团队
有赞技术团队
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
博客园 - 【当耐特】
Y
Y Combinator Blog
Last Week in AI
Last Week in AI
MongoDB | Blog
MongoDB | Blog
G
Google Developers Blog
博客园 - 三生石上(FineUI控件)
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
大猫的无限游戏
大猫的无限游戏
罗磊的独立博客
The Cloudflare Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
V2EX
博客园 - 司徒正美

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
An Empirical Study of Developers' Discussions about Secur...
Roland Croft, Yongzheng Xie, Mansooreh Zahedi, M. Ali Babar, Chr · 2021-07-29 · via cs.SE updates on arXiv.org

Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration of security in the choice of a programming language may lead to potential ramifications for secure development. Whilst theoretical analysis of the supposed security properties of different programming languages has been conducted, there has been relatively little effort to empirically explore the actual security challenges experienced by developers. We have performed a large-scale study of the security challenges of 15 programming languages by quantitatively and qualitatively analysing the developers' discussions from Stack Overflow and GitHub. By leveraging topic modelling, we have derived a taxonomy of 18 major security challenges for 6 topic categories. We have also conducted comparative analysis to understand how the identified challenges vary regarding the different programming languages and data sources. Our findings suggest that the challenges and their characteristics differ substantially for different programming languages and data sources, i.e., Stack Overflow and GitHub. The findings provide evidence-based insights and understanding of security challenges related to different programming languages to software professionals (i.e., practitioners or researchers). The reported taxonomy of security challenges can assist both practitioners and researchers in better understanding and traversing the secure development landscape. This study highlights the importance of the choice of technology, e.g., programming language, in secure software engineering. Hence, the findings are expected to motivate practitioners to consider the potential impact of the choice of programming languages on software security.