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

OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
J
Java Code Geeks
Blog — PlanetScale
Blog — PlanetScale
F
Fortinet All Blogs
腾讯CDC
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
WordPress大学
WordPress大学
雷峰网
雷峰网
小众软件
小众软件
D
DataBreaches.Net
V
Visual Studio Blog
博客园 - Franky
IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog RSS Feed
博客园 - 聂微东
T
Tailwind CSS Blog
有赞技术团队
有赞技术团队
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
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
云风的 BLOG
云风的 BLOG

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
The Impact of a Major Security Event on an Open Source Pr...
James Walden · 2020-05-29 · via cs.SE updates on arXiv.org

Context: The Heartbleed vulnerability brought OpenSSL to international attention in 2014. The almost moribund project was a key security component in public web servers and over a billion mobile devices. This vulnerability led to new investments in OpenSSL. Objective: The goal of this study is to determine how the Heartbleed vulnerability changed the software evolution of OpenSSL. We study changes in vulnerabilities, code quality, project activity, and software engineering practices. Method: We use a mixed methods approach, collecting multiple types of quantitative data and qualitative data from web sites and an interview with a developer who worked on post-Heartbleed changes. We use regression discontinuity analysis to determine changes in levels and slopes of code and project activity metrics resulting from Heartbleed. Results: The OpenSSL project made tremendous improvements to code quality and security after Heartbleed. By the end of 2016, the number of commits per month had tripled, 91 vulnerabilities were found and fixed, code complexity decreased significantly, and OpenSSL obtained a CII best practices badge, certifying its use of good open source development practices. Conclusions: The OpenSSL project provides a model of how an open source project can adapt and improve after a security event. The evolution of OpenSSL shows that the number of known vulnerabilities is not a useful indicator of project security. A small number of vulnerabilities may simply indicate that a project does not expend much effort to finding vulnerabilities. This study suggests that project activity and CII badge best practices may be better indicators of code quality and security than vulnerability counts.