Regression testing that keeps up with every change without slowing every release
Each release can break something that worked last week. We build and maintain a regression suite that is weighted by risk, runs where it is cheapest, and stays trustworthy because someone is accountable for it.
Overview
What it is. Regression testing re-checks existing behaviour after a change, such as a new feature, a fix, a dependency upgrade or a configuration update, to confirm nothing that used to work has stopped working.
Who it is for. Teams releasing frequently, products with many integrations, and teams whose manual regression cycle has become the longest part of the release.
Why it matters
The problem it addresses. Manual regression grows linearly with the product and eventually blocks releases. Automated regression, left unattended, fills with flaky and obsolete tests until the signal is no longer trusted.
When it matters most. When release cycles are limited by regression effort, when bugs keep reappearing in old areas, or when a red build no longer provokes any reaction.
What happens without it. Either releases slow down to protect quality, or they go out with known blind spots. Both cost more than a suite that is small, current and trusted.
What we test
Regression suite design in tiers
build-level smoke, per-release risk-based, and scheduled broad coverage
Automation of stable, high-value checks across web, mobile and API layers
Retained manual and exploratory regression where judgment beats scripting
Confirmation testing of fixes, kept distinct from regression checks
Flaky test quarantine and root-cause review
Suite maintenance
pruning obsolete tests, updating after product changes, tracking runtime
Impact-based selection of what to run for a given change
How UpDoer approaches Regression Testing
Understand
Requirements, tickets, code diffs and product context.
We review the existing suite, recent escaped defects and the release cadence, to see what is run, what it costs and what keeps getting through.
Risk Mapping
Where a failure would cost the most.
We map which areas change most and where a failure would hurt most, so coverage follows risk and not module size.
Test Strategy
Coverage chosen, test cases drafted.
We decide what to automate, what stays manual, and what to delete, and set the tiers and their run times.
Execute
Manual and automated testing runs.
Automated tests run on every build and release candidate; manual exploratory passes cover new and unstable areas.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps identify which areas a change is likely to affect, drafts scripts for stable flows, and proposes locator repairs when the interface changes.
Human Verification
A QA engineer checks every flagged result.
A QA engineer reviews every locator repair and every selection decision, triages failures, and decides whether a failing test is a defect, a flaw in the test or an intended change.
Report & Improve
The verified quality signal, then better coverage.
Each release gets a regression report: what ran, what failed and why, what was skipped and the risk of skipping it. The suite is pruned and extended from the findings.
Regression: protecting what already works
01
Where it breaks
Old bugs reappearFlaky red buildsRelease waits on retest02
Test workflow
ChangeRe-runCompareConfirm03
AI assists
Flag impacted areasPropose locator fixesGroup failures04
Humans decide
Review each repairReal defect or test flaw?05
Release signal
Run and skipped listsRisk of skippingGo / no-go by a person
Illustrative workflow. Not a client result or a measured outcome.
Regression suite tiers
| Tier | Runs when | What it protects | AI assistance | Human role |
|---|---|---|---|---|
| Build smoke | Every commit or build | Application starts; core journeys alive | Repairs trivial locator breaks | Reviews repairs; owns the stop decision |
| Risk-based release | Each release candidate | Highest-impact journeys and recently changed areas | Suggests impacted areas from the change | Confirms scope; adds exploratory checks |
| Broad scheduled | Nightly or weekly | Long-tail features and configurations | Groups failures by cause | Triages; prunes obsolete tests |
| Confirmation | After each fix | The reported defect is actually gone | Links the fix to the original case | Verifies; closes the defect |
| Exploratory | On changed or new areas | What scripts do not yet cover | Suggests charters from the change | Explores and records new checks |
| Flaky quarantine | Continuous | Trust in the signal | Flags tests that fail intermittently | Finds the cause or retires the test |
Tier: Build smoke
- RUNS WHEN
- Every commit or build
- WHAT IT PROTECTS
- Application starts; core journeys alive
- AI ASSISTANCE
- Repairs trivial locator breaks
- HUMAN ROLE
- Reviews repairs; owns the stop decision
Tier: Risk-based release
- RUNS WHEN
- Each release candidate
- WHAT IT PROTECTS
- Highest-impact journeys and recently changed areas
- AI ASSISTANCE
- Suggests impacted areas from the change
- HUMAN ROLE
- Confirms scope; adds exploratory checks
Tier: Broad scheduled
- RUNS WHEN
- Nightly or weekly
- WHAT IT PROTECTS
- Long-tail features and configurations
- AI ASSISTANCE
- Groups failures by cause
- HUMAN ROLE
- Triages; prunes obsolete tests
Tier: Confirmation
- RUNS WHEN
- After each fix
- WHAT IT PROTECTS
- The reported defect is actually gone
- AI ASSISTANCE
- Links the fix to the original case
- HUMAN ROLE
- Verifies; closes the defect
Tier: Exploratory
- RUNS WHEN
- On changed or new areas
- WHAT IT PROTECTS
- What scripts do not yet cover
- AI ASSISTANCE
- Suggests charters from the change
- HUMAN ROLE
- Explores and records new checks
Tier: Flaky quarantine
- RUNS WHEN
- Continuous
- WHAT IT PROTECTS
- Trust in the signal
- AI ASSISTANCE
- Flags tests that fail intermittently
- HUMAN ROLE
- Finds the cause or retires the test
Where AI helps
- Flagging the areas a code or configuration change is most likely to affect, as a suggested starting subset for a person to confirm.
- Drafting automation scripts for stable flows from existing manual cases.
- Proposing repairs when a locator breaks after a UI change, so the suite does not fail wholesale on cosmetic updates.
- Classifying failures and spotting patterns across runs to separate recurring defects from flaky tests.
- Summarising a run for the release decision, with the skipped areas listed.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Reviewing each AI-proposed locator repair, since a repaired test can hide a real interface problem.
- Deciding which tests to retire, which to automate and which should stay manual.
- Judging whether a failure is a product defect, a test defect or an intended behaviour change.
- Exploring newly changed areas where no scripted check exists yet.
- Owning the go or no-go recommendation, including the risk of anything not run.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
How much of our regression can be automated?
It depends on how stable the flows are and how often they change. We recommend automating stable, high-value checks and keeping manual or exploratory coverage where scripts would be expensive to maintain. We do not set a target percentage in advance.
Do you use AI to pick which tests run?
AI can suggest which areas a change is likely to affect. A QA engineer confirms the subset, and the report lists anything skipped so the risk is visible.
What about flaky tests?
Flaky tests are quarantined with an owner and a deadline, and the root cause is investigated. Rerunning until green is not accepted as a fix.