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

Engineering at Meta
Engineering at Meta
雷峰网
雷峰网
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog
Y
Y Combinator Blog
WordPress大学
WordPress大学
Microsoft Azure Blog
Microsoft Azure Blog
小众软件
小众软件
G
Google Developers Blog
云风的 BLOG
云风的 BLOG
罗磊的独立博客
博客园 - 三生石上(FineUI控件)
博客园 - 叶小钗
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
量子位
The Cloudflare Blog
T
The Blog of Author Tim Ferriss
博客园_首页
B
Blog RSS Feed
Hugging Face - Blog
Hugging Face - Blog
IT之家
IT之家
阮一峰的网络日志
阮一峰的网络日志
L
LangChain Blog
宝玉的分享
宝玉的分享

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
Agent Based Software Testing Framework (ABSTF) for Applic...
K. Karnavel, V. Divya, Gnanakeerthika, P. Karthika · 2013-07-12 · via cs.SE updates on arXiv.org

Software testing framework can be stated as the process of verifying and validating that a computer program/application works as expected and meets the requirements of the user. Usually testing can be done manually or using tools. Manual testing can be time consuming and tedious, also it requires heavy investment of human resources. Testing tools in fact have many advantages and are widely used nowadays, but also has several disadvantages. One particular problem is that human intervention is no longer needed. Testing tools are of high cost and so, it cannot be used for small companies. Hence in this paper we propose Agent based testing, which is a fast growing approach in testing field. The proposed system (ABSTF) has to reduce the application testing moment, easily find out bug and solve the bug by Regression Testing. Here by we are going to use a safe efficient regression selection technique algorithm to selectively retest the modified program. We also use Traceability relation of completeness checking for agent oriented system, which identifies missing elements in entire application and classifies defect in the testing moment. With the ability of agents to act autonomously, monitoring code changes and generating test cases for the changed version of code can be done dynamically.