GitHub Copilot
AI
Commercial
<What you get/>
Faster scaffolding of page objects and repetitive test structures
More engineer time spent on test design and risk analysis rather than boilerplate
Every generated assertion reviewed against the actual requirement before it lands
A check on your IP and data-handling position before we enable it on your codebase
What it is
GitHub Copilot suggests code inline as an engineer types, trained on public repositories. For test automation it is most useful on boilerplate — assertion blocks, fixture setup, parameterised data sets — where the shape of the code is predictable and the engineer's job is to check the suggestion rather than compose it from scratch.
How PerfectQA uses GitHub Copilot
Copilot is an editor-level accelerator for our engineers, not something that appears in your deliverable. Its practical value on QA work is the repetitive layer: page object scaffolding, assertion boilerplate, and the fifth variation of a data-driven test where the shape is already established and only the values change. It is genuinely good at that and genuinely unreliable at test design — it will happily produce a test that runs, passes, and proves nothing, because it is completing a pattern rather than reasoning about risk. So we use it inside a review discipline: suggestions are a starting draft, and every assertion is checked against the actual requirement before it lands. On projects with strict IP or data handling requirements we confirm the client’s position on editor-level AI tooling before enabling it, rather than assuming.
Category
AI
In our stack
3 years
Projects
11 delivered
Frequent questions
Does Copilot see our source code?
Can it write our tests for us?
Does this mean fewer engineer hours?
What if we don't allow AI tooling?
Every stack is different
Tell us yours, and we’ll show you where this fits
