Artem Golubev || testRigor Trendjacking Radar (14d)

Before engineering teams hand ML system development to agentic models, independent evaluation standards must be established.

MLExpert reports lifting...

Activision's QA workforce has shrunk nearly one-third since union formation, from ~600 to just over 400, raising questions about sustainable workloads...

Production testing adds confidence only when synthetic checks, canary releases, feature flags, and observability tightly constrain the risk.

• Not a...

Two recent papers show verification of agent behavior outperforms pure generation for reliability and efficiency.

• SpecAgent shifts strong models...

The article draws a clear line: AI can increasingly handle test creation, execution, and maintenance, while broader QA activities like exploratory...

Writing tests is rarely the real expense in mobile QA. Locator work, breakage, and upkeep dominate the lifecycle.

• In-house: 1,092 hours per...

• 60% of organizations now ship untested AI-generated code, raising defect and compliance risks.
• Probabilistic AI outputs demand automated...

QA teams should treat test repositories and execution results as sensitive systems when granting AI agents access through MCP.

• Agents read tests...

An AI usage policy is a quality and release-control mechanism, not just compliance paperwork. It requires approved tools lists, strict data-handling...

A proposer agent studies solver rollouts then builds and tests new RL environments in a closed loop. This makes environment quality and embedded testing the new critical assurance layer for autonomous AI systems.

CI/CD automates code movement from commit to release, turning DevOps collaboration into repeatable delivery. Without strong continuous testing providing trustworthy signals, teams simply accelerate the spread of defects as release velocity increases.

Automation delivers the biggest ROI when it removes repetitive maintenance, manual regression testing, and reactive support work—not just by...

AI coding agents tackling long-horizon tasks succeed or fail based as much on harness quality as on the underlying model. Doc2Harness reveals that...

EU regulators now demand verifiable proof of controls, logs, and remediation after AI model testing incidents, not just internal claims of safety. For...

Mutation testing exposes AI-generated tests that pass without catching real logic flaws by deliberately breaking code and checking detection. Volume of automated tests means little if they fail to verify actual system behavior.

Functional testing confirms software meets business requirements and protects end-to-end user workflows from the user's perspective, turning release...

智能体平台的自主运行不等于放任决策，而是依赖渐进式引导、观测与反馈机制。工程领导者应将其视为需持续验证和回归测试的软件系统，才能在企业场景中实现可控自愈。

testRigor targets teams replacing brittle Selenium, Appium or Cypress suites with plain-English end-to-end tests powered by generative AI and...

AI QA now evaluates 100% of customer conversations instead of the typical 2-3% samples, scoring them in real time against customizable business...

Engineering leaders should limit E2E tests to a handful of high-value user journeys.

• Maintenance burden dominates: E2E tests are slow, flaky, and...