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risk-based testing

17 articles · Page 1 of 1
Woodpecker Software Testing
Woodpecker Software Testing
Sep 8, 2026 · R&D Management

2026 Test Coverage: From Metric Traps to Semantic-Aware, Risk-Weighted Quality Engines

The article argues traditional line coverage metrics are inadequate for 2026's AI-native and edge systems, proposing three advances: Semantic-Aware Coverage using LLMs to filter ghost paths, Dynamic Weighted Coverage prioritizing high-risk code via architecture, history, and runtime weights, and a Coverage-First paradigm embedding coverage goals into design and CI pipelines.

LLM-enhanced testingcoverage-firstdynamic weighted coverage
0 likes · 7 min read
2026 Test Coverage: From Metric Traps to Semantic-Aware, Risk-Weighted Quality Engines
FunTester
FunTester
Aug 28, 2026 · Artificial Intelligence

Where AI Really Belongs in Software Testing

The article examines how AI can assist various testing tasks—such as organizing test plans, extending existing test suites, generating data, and summarizing results—while warning that core analysis, risk assessment, and collaborative understanding must remain human‑driven.

AI testingTest Data Generationrisk-based testing
0 likes · 17 min read
Where AI Really Belongs in Software Testing
Data Bricklaying Diary
Data Bricklaying Diary
Aug 26, 2026 · Artificial Intelligence

AI Evaluation Sets ≠ Training Holdouts: Golden Data, Hard Cases, Contamination & Regression

This article explains why enterprise AI evaluation requires purpose-built datasets — golden baselines, challenge cases, red-team tests, regression suites, and online replays — designed from capability questions, risk lists, and real failures, with structured answers, contamination governance, tiered LLM judging, and version-locked regression pipelines gated by risk-based release thresholds.

AI evaluationLLM-as-a-Judgechallenge set
0 likes · 24 min read
AI Evaluation Sets ≠ Training Holdouts: Golden Data, Hard Cases, Contamination & Regression
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
FunTester
FunTester
Jul 5, 2026 · Industry Insights

Why Traditional Testing No Longer Suffices: Emerging Risk‑Based and AI‑Driven Strategies

Software testing is shifting from defect detection to continuous risk assurance, with AI guiding test design, autonomous test systems handling routine validation, and quality engineering integrating across the development lifecycle to quantify business risk, ensure compliance, and support rapid, reliable releases by 2026.

AI testingContinuous DeliveryDevOps
0 likes · 13 min read
Why Traditional Testing No Longer Suffices: Emerging Risk‑Based and AI‑Driven Strategies
ArcThink
ArcThink
May 31, 2026 · Artificial Intelligence

Why AI’s “I’ve Tested It” Isn’t Enough: Implementing a Verification Gate Workflow

The article explains that AI agents often claim tasks are complete without providing verifiable evidence, and introduces a Verification Gate that requires concrete command, result, coverage, and risk information—structured by risk‑based layers, hooks, and subagents—to ensure honest and traceable completion of AI‑driven code changes.

AI agentsHooksVerification Gate
0 likes · 16 min read
Why AI’s “I’ve Tested It” Isn’t Enough: Implementing a Verification Gate Workflow
转转QA
转转QA
May 28, 2026 · Operations

How AI Is Redefining QA: Lessons from an On‑Site Recycling Team

In the AI coding era, traditional test‑after‑code practices cause missed bugs, so a recycling‑service QA team adopts intent‑driven testing, business‑view AI code review, and risk‑focused automation to transform testers into AI‑assisted quality strategists.

AI code reviewAI testingQA Automation
0 likes · 10 min read
How AI Is Redefining QA: Lessons from an On‑Site Recycling Team
Woodpecker Software Testing
Woodpecker Software Testing
Mar 3, 2026 · Fundamentals

5 Major Test Coverage Pitfalls That Undermine Software Quality

The article reveals five common misconceptions in test coverage optimization—confusing coverage with verification, chasing 100% branch coverage, over‑counting non‑business code, ignoring distributed‑system interactions, and treating coverage as a KPI—showing how they lead to defects despite high coverage percentages.

Contract Testingchaos engineeringmicroservices
0 likes · 8 min read
5 Major Test Coverage Pitfalls That Undermine Software Quality
FunTester
FunTester
Sep 25, 2023 · Fundamentals

Balancing Test Automation: When to Automate, What to Automate, and Common Pitfalls

The article examines why software testing inevitably consumes time and resources, explains the limits of both manual and automated testing, outlines scenarios best suited for automation such as regression and performance testing, and warns against unrealistic expectations, unnecessary tests, and security blind spots.

continuous integrationregression-testingrisk-based testing
0 likes · 9 min read
Balancing Test Automation: When to Automate, What to Automate, and Common Pitfalls
Baidu Tech Salon
Baidu Tech Salon
Apr 13, 2023 · Fundamentals

Understanding Software Quality: From Usability to Test Modeling and Cost Analysis

Understanding software quality involves three layers—Usable, Good‑to‑use, and Love‑to‑use—linked to business value, a quantitative loss model, testing’s feedback role, risk‑based test classification, defect‑handling costs, and emerging AI tools like TestGPT that automate test generation and decision‑making.

QAcost analysisquality modeling
0 likes · 44 min read
Understanding Software Quality: From Usability to Test Modeling and Cost Analysis
Baidu Intelligent Testing
Baidu Intelligent Testing
Apr 12, 2023 · Fundamentals

Understanding Software Quality: From Usability Layers to Testing Practices and Cost Management

This comprehensive article explains software quality through three layers—usability, usefulness, and loveability—introduces a quality loss model, details testing concepts and phases, discusses cost of quality, risk‑based testing, and the future of TestGPT, while emphasizing the need for technical research and automation in QA.

QASoftware Engineeringquality cost
0 likes · 45 min read
Understanding Software Quality: From Usability Layers to Testing Practices and Cost Management
Baidu Geek Talk
Baidu Geek Talk
Apr 12, 2023 · Industry Insights

From Usable to Loveable: Redefining Software Quality and Testing Costs

This article explores software quality through three layers—usability, desirability, and loveability—introduces a quantitative quality model, breaks down the testing lifecycle, examines testing classifications, analyzes the cost of defect recall, and proposes risk‑based testing and AI‑driven TestGPT as future solutions.

QATestGPTautomation
0 likes · 47 min read
From Usable to Loveable: Redefining Software Quality and Testing Costs
DevOps
DevOps
Nov 11, 2020 · Fundamentals

Why “Shift‑Left Testing” Is a Misleading Concept

The article argues that the so‑called “shift‑left testing” is not a new concept but a rebranding of long‑standing testing principles, critiques its misinterpretation as moving test engineers left, and advocates for developers to own unit testing while professional testers focus on risk‑based, comprehensive quality assurance across the software lifecycle.

development practicesquality assurancerisk-based testing
0 likes · 17 min read
Why “Shift‑Left Testing” Is a Misleading Concept
FunTester
FunTester
Jun 3, 2020 · Operations

How to Slash Software Testing Costs Without Sacrificing Quality

This article presents practical strategies for reducing software testing expenses—including early testing, balanced documentation, risk‑based testing, leveraging production data, prioritizing API over UI automation, adopting open‑source tools, optimizing infrastructure, training, process improvements, and selective outsourcing—while maintaining overall product quality.

Cost OptimizationOpen Source Toolsrisk-based testing
0 likes · 9 min read
How to Slash Software Testing Costs Without Sacrificing Quality
360 Tech Engineering
360 Tech Engineering
Jan 24, 2019 · Fundamentals

The Truth About Test Automation: Myths, Maintenance, and Balancing Manual and Automated Testing

This article debunks common myths about test automation, explains why scripts require ongoing maintenance, discusses why full automation is impossible, and offers practical guidance on balancing automated and manual testing using risk‑based, conversation‑driven, and exploratory approaches to maximize value.

maintenancemanual testingrisk-based testing
0 likes · 7 min read
The Truth About Test Automation: Myths, Maintenance, and Balancing Manual and Automated Testing
Ctrip Technology
Ctrip Technology
Jul 27, 2017 · R&D Management

Innovations in Mobile Testing under Agile: Risk‑Based Testing, Shift‑Left Practices, and Automation Strategies

The article explores the challenges of mobile testing in fast‑paced agile environments and presents a comprehensive approach that combines risk‑based testing, shift‑left quality assurance, layered automation, service‑interface testing, UI automation, and supporting infrastructure to improve efficiency and product quality.

UI automationmobile testingmock services
0 likes · 14 min read
Innovations in Mobile Testing under Agile: Risk‑Based Testing, Shift‑Left Practices, and Automation Strategies