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

G
Google Developers Blog
D
Docker
Stack Overflow Blog
Stack Overflow Blog
GbyAI
GbyAI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google DeepMind News
Google DeepMind News
M
MIT News - Artificial intelligence
H
Help Net Security
T
Tailwind CSS Blog
Engineering at Meta
Engineering at Meta
L
LangChain Blog
MongoDB | Blog
MongoDB | Blog
腾讯CDC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
S
SegmentFault 最新的问题
博客园 - 司徒正美
C
Check Point Blog
B
Blog
Y
Y Combinator Blog
Microsoft Azure Blog
Microsoft Azure Blog
P
Proofpoint News Feed
F
Fortinet All Blogs
美团技术团队
D
DataBreaches.Net

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
Flaky Test Sanitisation via On-the-Fly Assumption Inferen...
Jens Dietrich, Shawn Rasheed, Amjed Tahir · 2022-08-02 · via cs.SE updates on arXiv.org

Flaky tests cause significant problems as they can interrupt automated build processes that rely on all tests succeeding and undermine the trustworthiness of tests. Numerous causes of test flakiness have been identified, and program analyses exist to detect such tests. Typically, these methods produce advice to developers on how to refactor tests in order to make test outcomes deterministic. We argue that one source of flakiness is the lack of assumptions that precisely describe under which circumstances a test is meaningful. We devise a sanitisation technique that can isolate f laky tests quickly by inferring such assumptions on-the-fly, allowing automated builds to proceed as flaky tests are ignored. We demonstrate this approach for Java and Groovy programs by implementing it as extensions for three popular testing frameworks (JUnit4, JUnit5 and Spock) that can transparently inject the inferred assumptions. If JUnit5 is used, those extensions can be deployed without refactoring project source code. We demonstrate and evaluate the utility of our approach using a set of six popular real-world programs, addressing known test flakiness issues in these programs caused by dependencies of tests on network availability. We find that our method effectively sanitises failures induced by network connectivity problems with high precision and recall.