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

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
云风的 BLOG
云风的 BLOG
IT之家
IT之家
C
Check Point Blog
T
The Blog of Author Tim Ferriss
S
SegmentFault 最新的问题
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
美团技术团队
M
MIT News - Artificial intelligence
Jina AI
Jina AI
Blog — PlanetScale
Blog — PlanetScale
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
F
Fortinet All Blogs
V
Visual Studio Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
T
Tailwind CSS Blog
Hugging Face - Blog
Hugging Face - Blog
MyScale Blog
MyScale Blog
爱范儿
爱范儿
The Cloudflare Blog
博客园 - 三生石上(FineUI控件)

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
Fast and Precise On-the-fly Patch Validation for All
Lingchao Chen, Lingming Zhang · 2020-07-22 · via cs.SE updates on arXiv.org

Generate-and-validate (G&V) automated program repair (APR) techniques have been extensively studied during the past decade. Meanwhile, such techniques can be extremely time-consuming due to manipulation of the program code to fabricate a large number of patches and also repeated executions of tests on patches to identify potential fixes. PraPR, a recent G&V APR technique, reduces these costs by modifying program code directly at the level of compiled bytecode, and further performing on-the-fly patching by allowing multiple patches to be tested within the same JVM session. However, PraPR is limited due to its pattern-based, bytecode-level nature and it is basically unsound/imprecise as it assumes that patch executions do not change global JVM state and affect later patch executions on the same JVM session. Inspired by the PraPR work, we propose a unified patch validation framework, named UniAPR, which aims to speed up the patch validation for both bytecode and source-code APR via on-the-fly patching; furthermore, UniAPR addresses the imprecise patch validation issue by resetting the JVM global state via runtime bytecode transformation. We have implemented UniAPR as a fully automated Maven Plugin. We have also performed the first study of on-the-fly patch validation for state-of-the-art source-code-level APR. Our experiments show the first empirical evidence that vanilla on-the-fly patch validation can be imprecise/unsound; in contrast, our UniAPR framework can speed up state-of-the-art APR by over an order of magnitude without incurring any imprecision in patch validation, enabling all existing APR techniques to explore a larger search space to fix more bugs in the near future. Furthermore, UniAPR directly enables hybrid source and bytecode APR to fix substantially more bugs than all state-of-the-art APR techniques (under the same time limit) in the near future.