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AI-assisted testing

4 articles · Page 1 of 1
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 8, 2026 · Fundamentals

JiTTesting: Just-in-Time Testing for AI-Speed Code Changes

JiTTesting introduces a dual-track testing strategy—permanent hardening tests for regression prevention and temporary catching tests generated on-demand to detect behavioral differences in AI-generated code diffs—using parallel pipelines, automated noise reduction, and human-in-the-loop validation to keep pace with rapid AI-driven development.

AI-assisted testingJiTTestingLLM-based testing
0 likes · 10 min read
JiTTesting: Just-in-Time Testing for AI-Speed Code Changes
Woodpecker Software Testing
Woodpecker Software Testing
Aug 11, 2026 · R&D Management

Optimizing Test Coverage: The Key Engine Driving Team Transformation

The article explains how shifting test coverage from a simple metric to a diagnostic tool—aligned with business risk, enriched by AI analysis, and embedded in team roles and organizational contracts—can dramatically improve software quality, delivery speed, and overall engineering maturity.

AI-assisted testingcontinuous integrationquality engineering
0 likes · 8 min read
Optimizing Test Coverage: The Key Engine Driving Team Transformation
Woodpecker Software Testing
Woodpecker Software Testing
Apr 29, 2026 · Artificial Intelligence

Leveraging ChatGPT to Transform Software Development

The article explains how large language models like ChatGPT can assist software engineers across the entire development lifecycle—requirements, design, coding, testing, and operations—while emphasizing the need for human review due to hallucinations, and presents a PDCA‑style iterative workflow for effective human‑AI collaboration.

AI-assisted testingChatGPTLarge Language Models
0 likes · 4 min read
Leveraging ChatGPT to Transform Software Development
Woodpecker Software Testing
Woodpecker Software Testing
Apr 5, 2026 · Industry Insights

2026 Test Coverage Trends: From Sufficient to Precise Risk‑Driven Strategies

The article examines how test coverage in 2026 shifts from simple percentage goals to risk‑driven, AI‑enhanced, and visualized approaches, highlighting the RDC model, LLM‑assisted gap analysis, causal graph visualizations, and left‑right coverage governance across CI/CD and production environments.

AI-assisted testingCI/CD governanceObservability
0 likes · 7 min read
2026 Test Coverage Trends: From Sufficient to Precise Risk‑Driven Strategies