ISTQB Agile Technical Tester (ATT) - 2026

Explore how test first techniques such as TDD, BDD, and ATDD shape agile development, and examine why experience-based testing, refactoring, code reviews, and static analysis matter.

Apply test driven development, behavior driven development, and acceptance test driven development in agile projects; analyze the product backlog to introduce ATDD and improve software quality for users.

Learn how test-driven development (TDD) designs and tests code first, using red-green-refactor cycles, unit tests, and assertions to deliver quality software in agile sprints.

Adopt behavior driven development (bdd) to align developers, testers, and business stakeholders around customer expectations, translating requirements into human readable, executable scenarios using given, when, then.

Explore acceptance test driven development (atdd) to create high level, human readable acceptance tests from user stories, using specification by example and boundary value analysis.

Combine automated, exploratory, and manual black-box tests by weighing factors like development approach, team skills, risk levels, and project deadlines to optimize quality in agile projects.

Create and interpret test charters to guide structured exploratory testing of user stories. Apply requirements engineering insights and heuristics, and document results in debriefings within agile teams.

Refactoring cleans up existing code and tests without changing behavior, improving readability and flexibility. Learn to maintain test suites during short sprints through five steps: identification, analysis, refactor, rerun, evaluate.

Explore static code analysis tools that examine code without execution, identify problems, and suggest indicators for further assessment, with linters like ESLint and Pylint offering automatic fixes.

Master agile testing concepts through review questions on test driven development, behavior driven development, stakeholder requirements, test cases, test charters, risk analysis, and defect identification.

Learn how data driven testing separates test data from logic, enabling repeated test execution with varying inputs and supporting agile teams with easy data management and faster automation.

Explore keyword-driven testing, a readable, maintainable approach using keywords from business domain to drive test automation across UI, API, and unit tests, with DSL/BDD styles and living documentation.

Apply test automation to a given test approach to boost efficiency, performance, reliability, and early defect discovery, aligned with the project environment and agile lifecycle.

Automate testing and deployment in agile projects to sustain code quality, achieve continuous deployment, balance test suite growth with maintenance, risk analysis, and exploratory testing.

Assess keyword driven versus data driven test automation for teams with minimal programming, noting keyword driven tests need no code, while data driven testing reduces maintenance by data reuse.

Apply data driven and keyword driven testing with continuous integration, testing, and delivery, track code change regressions, and leverage service virtualization for realistic early test data.

Learn how continuous integration merges code changes into a shared repository, runs automated tests, and delivers reliable software daily by using source control, build tools, and notifications.

Discover how continuous integration shapes testing with automated tests, test configurations, and risk-based prioritization across environments, guided by the test automation pyramid.

Explore how continuous testing provides early, automated feedback across the IDE, CI, and continuous delivery and deployment, enabling rapid, defect-free releases to staging and production.

Harness service virtualization to create a virtual service that imitates real operations. Test earlier with stubs, drivers, and mocks, and simulate stateful data and variability.

See how continuous integration supports testing by triggering automated tests and preventing new changes from breaking existing functionality.

Explore the five solid principles—single responsibility, open close, Liskov substitution, interface segregation, and dependency inversion—and how they shape modular, maintainable software.