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

Google DeepMind News
Google DeepMind News
F
Fortinet All Blogs
量子位
G
Google Developers Blog
J
Java Code Geeks
N
Netflix TechBlog - Medium
博客园 - 聂微东
宝玉的分享
宝玉的分享
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
月光博客
月光博客
The Cloudflare Blog
Apple Machine Learning Research
Apple Machine Learning Research
爱范儿
爱范儿
雷峰网
雷峰网
M
MIT News - Artificial intelligence
T
Tailwind CSS Blog
V
Visual Studio Blog
阮一峰的网络日志
阮一峰的网络日志
博客园 - 三生石上(FineUI控件)
Microsoft Azure Blog
Microsoft Azure Blog
aimingoo的专栏
aimingoo的专栏
Martin Fowler
Martin Fowler
有赞技术团队
有赞技术团队
T
The Blog of Author Tim Ferriss

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
On Introducing Automatic Test Case Generation in Practice...
Matteo Brunetto, Giovanni Denaro, Leonardo Mariani, Mauro Pezzè · 2021-02-28 · via cs.SE updates on arXiv.org

The level and quality of automation dramatically affects software testing activities, determines costs and effectiveness of the testing process, and largely impacts on the quality of the final product. While costs and benefits of automating many testing activities in industrial practice (including managing the quality process, executing large test suites, and managing regression test suites) are well understood and documented, the benefits and obstacles of automatically generating system test suites in industrial practice are not well reported yet, despite the recent progresses of automated test case generation tools. Proprietary tools for automatically generating test cases are becoming common practice in large software organisations, and commercial tools are becoming available for some application domains and testing levels. However, generating system test cases in small and medium-size software companies is still largely a manual, inefficient and ad-hoc activity. This paper reports our experience in introducing techniques for automatically generating system test suites in a medium-size company. We describe the technical and organisational obstacles that we faced when introducing automatic test case generation in the development process of the company, and present the solutions that we successfully experienced in that context. In particular, the paper discusses the problems of automating the generation of test cases by referring to a customised ERP application that the medium-size company developed for a third party multinational company, and presents ABT2.0, the test case generator that we developed by tailoring ABT, a research state-of-the-art GUI test generator, to their industrial environment. This paper presents the new features of ABT2.0, and discusses how these new features address the issues that we faced.