The described goal matches the defining characteristics of keyword-driven testing: tests are expressed using keywords (action words) that represent user operations, often arranged in tabular form with parameters/test data. TAE describes keyword-driven approaches as enabling non-programmers (e.g., test analysts) to create and maintain tests by combining high-level keywords such as “Open Browser,” “Click,” “Enter Text,” “Select,” “Verify Text,” etc., while the underlying automation framework maps those keywords to executable code. The use of files interpreted by a test execution tool is also typical: keyword tables (or similar structured specifications) are read and executed by the automation engine. Data-driven testing focuses on separating test logic from test data, typically running the same script multiple times with different datasets; it does not inherently require natural-language action words or tabular step definitions (though it can be combined). Linear scripting is code-centric and not aligned with analyst-authored natural language step tables. TDD is unrelated to the requirement of tabular, natural-language keyword specification for UI test steps. Therefore, keyword-driven testing is the best fit for the stated approach.

TAE guidance links maintainability and scalability to reducing duplication and encapsulating common actions behind reusable abstractions. For UI regression suites on existing web apps, capture/playback and linear scripting often produce brittle, duplicated sequences tightly coupled to UI details. They may be quick initially, but maintenance cost grows rapidly when locators, flows, or timing change. With a programming language that supports libraries—and a team with strong programming skills—TAE recommends structured scripting (often including modularization, reuse through functions/classes, and design patterns such as Page Object or similar abstractions). Structured scripting reduces maintenance by centralizing UI interaction logic (e.g., element locators and common workflows) so changes are made in one place. It also reduces effort to add new tests because test authors can compose new scenarios from existing reusable building blocks rather than duplicating low-level steps. TDD is a development practice and is not the primary approach for converting existing manual UI regression suites into automation; it does not directly describe how the UI tests should be structured. Capture/playback remains useful as a helper (e.g., for quickly discovering locators) but is not the best overall approach for long-term maintainability. Therefore, structured scripting best matches the stated goals.

In TAE, “design for testability” includes attributes that make it easier to create, execute, and maintain automated tests across levels (component, integration, system, UI). The need to define precisely which interfaces are available at different test levels—e.g., public APIs, service endpoints, message queues, UI automation hooks, test seams, logs, and internal test interfaces—maps most directly to architecture transparency . Architecture transparency concerns how clearly the system’s structure, layers, and accessible interfaces are documented and exposed so test automation can reliably connect to the right interaction points. This includes understanding which interfaces are stable, supported, and appropriate for each level of testing, and avoiding “guesswork” that increases brittleness. Controllability is about the ability to set inputs, states, and preconditions (e.g., reset data, seed databases, drive system state). Observability is about the ability to see outputs, internal states, and logs to assess outcomes. Autonomy concerns whether tests can run independently without external dependencies or manual intervention (e.g., isolated environments, stable test data). While controllability/observability/autonomy are critical for automation, the specific emphasis on “precisely defining which interfaces are available” is fundamentally an architectural transparency issue: clear interface availability and documentation enable correct, maintainable automation connections across test levels.

TAE describes quality gates as defined checkpoints in pipelines where objective criteria determine whether the pipeline may proceed (e.g., thresholds, pass/fail rules, coverage, or risk-based acceptance). Automated tests at multiple levels can serve as such gates. In the build phase, CT and CIT are commonly used as strong, fast quality gates because they provide quick feedback on code correctness and integration of closely related components; failures typically block promotion. In the deployment phase, after provisioning and deploying into a test environment, automated System Testing and Acceptance Testing can also serve as quality gates for promoting a build to later stages or release candidates, especially when the organization relies on automated regression and automated acceptance criteria for release decisions. While ST/AT may take longer and may be more prone to environmental factors, TAE still supports using them as gates when they are sufficiently stable, relevant, and aligned with release risk. The scenario explicitly places ST/AT in a separate triggered pipeline, which still qualifies as a gating mechanism if downstream promotion depends on its outcome. Therefore, both CT-CIT and ST-AT can act as quality gates.