Quality Engineering

Overview

Quality engineering standards require changes, including work and functionality that utilizes AI, to be verified for correctness, performance, security, and accessibility.

Choose a standard below to read it in full.

Standards

Test Automation

Test automation applies risk, value, testability, and maintenance considerations to produce reliable and appropriately scoped evidence.

AI-Assisted Quality Engineering

AI-assisted quality engineering includes AI use in testing scope, keeps decisions with people, checks AI work independently, controls agents, and measures results.

Outcome Driven Assurance

Outcome driven assurance connects quality questions, acceptance criteria, tests, evidence, and findings to the decisions they support.

Evidence & Reporting

Quality evidence is retained in shared locations where responsible teams and decision-makers can access it while it remains relevant.

Quality Roles & Responsibilities

Quality and testing activities have explicit role assignments, shared accountability, and QA oversight across their lifecycle.

Unit Testing

Unit testing verifies one unit of code in isolation with external dependencies replaced by controlled test doubles.

Integration Testing

Integration testing verifies that components work correctly together in a fresh, isolated, and repeatable environment.

Test Data Management

Non-production environments use synthetic, de-identified, or purpose-built data and exclude unmodified production data.

Performance & Load Testing

Performance and load testing validates a service against defined performance and capacity targets under representative demand.

Application Security Testing

Application security testing uses techniques suited to each vulnerability class and verifies each finding's remediation.

Accessibility Testing

Accessibility is verified through automated scanning and testing with real assistive technology across public and internal interfaces.