Automation fails in a predictable way: the suite gets slow, then it gets flaky, then it moves to a nightly run, and then it is gone. This is the practice that reverses that — including a fixed three-to-six-week rescue for a suite the team has already stopped trusting.
A fixed-duration offer to rebuild a suite the team stopped trusting — the only fixed-length engagement published here.
Not marketing language: it is what the pod actually ran at Rocketium, alongside weekly standups.
On a published fintech programme, across multiple platforms, at about 90% coverage.
A mean response time hides the tail where the unhappiest users — and the support tickets — live.
Test automation here is a distinct discipline from quality assurance and from testing: it is building and maintaining automation frameworks wired into CI for fast, low-flake feedback. The framework separates test logic from locators and data, uses reusable components and stable waits so tests fail for real reasons rather than timing noise, and adds self-healing locators to absorb the cosmetic UI churn that used to break dozens of scripts a release.
Regression is selected rather than run wholesale — the full pack where it belongs and a targeted subset on every commit, chosen by what the change actually touches — and executed in parallel so the feedback loop stays inside the window a developer will wait for. Flake is treated as a first-class deliverable with a budget reviewed weekly, which is precisely what the pod did at Rocketium: rebuild around risk, wire into CI with parallel execution, and move regression from once a sprint to every deployment.
Migrating off a legacy suite happens in coverage order, never alphabetically. Every legacy script is scored on the business risk it protects, low-value scripts are retired rather than ported, and nothing is switched off until its replacement passes against the same build — so coverage never dips mid-transition.
The artefacts a pod leaves behind, and the rules it applies to them.
Test logic separated from locators and data, reusable components, deterministic waits and self-healing locators. The structure is the deliverable: a suite that is cheap to change survives, and one that is expensive to change gets abandoned.
The full pack where it belongs, and a targeted subset on every commit chosen by what the change actually touches. Once a suite takes longer than the team will wait, it moves to nightly, then to pre-release, and the feedback loop it existed to provide is gone.
Every build captures instrumented screens and compares them against an approved baseline per page, component and state. Intended changes are approved and the baseline updates in the same flow; unintended ones block the merge.
Responses validated against the OpenAPI, GraphQL or WSDL contract, so an undocumented change fails your build instead of surfacing as a consumer's production incident. OWASP API risks are covered in the same pass.
Not a pass or fail: the concurrency at which response time leaves its acceptable band, the resource that saturates first, and the evidence tying one to the other — reported as p95 and p99 distributions.
An emulator matrix for breadth in CI and a curated real-device set for the release gate, weighted to the handsets your own analytics show — because emulators do not reproduce camera and sensor behaviour, battery drain, thermal throttling, biometrics or push delivery.
Two shapes: build a suite that does not exist, or rescue one that does.
Every existing script is scored on the business risk it protects. Muted tests, duplicates and brittle scripts are identified for retirement rather than porting, and the flake sources are diagnosed before anything is rebuilt.
Framework skeleton, locator and data separation, reusable components and stable waits, wired into GitHub Actions, GitLab CI, Jenkins or Azure DevOps with parallel execution. First slice running inside the first week.
High-risk paths rebuilt first, and the old suite stays running until its replacement passes against the same build. Nothing is switched off on the promise that the new one is better.
Regression selection by change surface, visual diffs on the pull request, contract tests against the API spec, and a merge that blocks on an unintended difference rather than on a nightly report nobody reads.
Documentation and team training at handover, with the flake budget and the ownership model written down. The point is that your team runs it afterwards.
The people who call are usually inheriting a problem rather than starting one.
Manual regression is the reason deployment happens once a sprint. The measurable target is regression cycle time, and it is the number the engagement is held to.
The tests fail at random, so everyone ignores them. This is the rescue engagement: three to six weeks to triage the flake, restructure and re-establish that red means something.
Four tools, four maintenance burdens, four reporting formats and no single answer to the only question leadership asks — are we covered? Consolidation is a delivery model, not a tool purchase.
Sharp lines, because the neighbouring disciplines look similar from the outside.
By designing for stability from the start — separating test logic from locators, using deterministic waits and controlled test data — and diagnosing flakiness at its source rather than adding a retry. A stabilised suite where red means a real defect is the deliverable.
Yes. We assess current coverage and risk, build a maintainable framework around the highest-payback paths, and integrate it into CI — usually starting with a pilot suite to prove reliability before scaling.
In coverage order, never all at once. Each legacy script is scored on the business risk it protects, high-value tests are rebuilt first, duplicates and brittle scripts are retired rather than ported, and the old suite keeps running until its replacement passes against the same build.
It speeds up the two most expensive parts — authoring and maintenance. It does not replace judgement: a generated test still needs senior review to confirm it asserts something that actually matters.
A functional test asserts against the DOM and has no opinion about what was painted. A checkout button can render white on white, sit behind a modal overlay or be pushed below the fold and every functional assertion still passes. That gap is what visual diffing covers.
A load test measures behaviour at expected traffic. A stress test pushes past it to find the breaking point. A soak test holds moderate load for hours to surface memory leaks, connection-pool exhaustion and logs filling a disk. A spike test models sudden arrival.
Talk to the group and a senior lead scopes it in writing, or go straight to the service's own site and look at it yourself. Neither route commits you to the other.
Name the number you need to hit. A senior lead replies within one business day and a costed plan follows within three working days.
Appsierra's automation pages, covering unified automation, regression, visual, API, performance and mobile testing in detail.
Everything the group sells around Test automation — the company that delivers it, the nearest siblings, and the full list.