testRigor
Plain English test automation with AI-powered self-healing. Write tests in natural language without coding.
Overview
testRigor is an AI-driven test automation platform that allows testers to write tests in plain English (or 60+ other natural languages). Instead of CSS selectors or XPath, you write commands like "click on the Sign In button" or "enter a valid email address into the Email field." The AI engine interprets these commands and executes them against the application, adapting to UI changes automatically.
testRigor is particularly popular with teams that have non-technical QA engineers or business analysts who need to create and maintain tests without programming skills.
What it's used for
testRigor excels when:
- Non-technical testers create automation: Plain English syntax removes the coding barrier entirely.
- UI is highly dynamic: AI-based element finding is more resilient than traditional locators.
- Cross-platform coverage needed: Web, mobile web, and native mobile from the same tests.
- Exploratory testing integration: Convert manual exploratory sessions into automated tests.
Pros & Cons
Pros
- Plain English test authoring — no coding required
- AI element detection is highly resilient to UI changes
- Supports web, mobile web, and native mobile
- Integrated test management and reporting
- API testing supported within the same plain-English syntax
Cons
- Expensive: starts at ~$300/month for small teams
- Limited debugging capability for technical failures
- Can be slower than code-based tools for complex scenarios
- SaaS-only with limited on-premise options
- Smaller ecosystem and community than Selenium/Playwright
Platforms & Integrations
testRigor is a cloud-based SaaS platform. It tests web applications, mobile web, and native mobile apps (via integration with device clouds).
Pricing
| Tier | Cost | Includes |
|---|---|---|
| Starter | From ~$300/mo | Up to 5 users, basic test execution |
| Professional | Custom | Unlimited users, advanced AI features, API access |
| Enterprise | Custom | SSO, dedicated support, custom integrations, on-premise option |
NZ Context
testRigor is less common in NZ than mabl or Testim, but it is gaining traction among teams with business-focused QA departments. The plain-English approach resonates with NZ organisations upskilling manual testers into automation roles. However, the price point is a barrier for smaller NZ companies.
Alternatives
- mabl — Similar AI self-healing but with a recorder-based approach instead of plain English.
- Testsigma — Another plain-English AI testing platform, often more affordable.
- Robot Framework — Free keyword-driven framework with natural-language-like syntax.
When to choose testRigor
A quick decision guide for NZ teams evaluating no-code AI test automation options.
| Choose testRigor when… | Choose something else when… | Combine with… |
|---|---|---|
| Your QA team has strong domain knowledge but limited coding skills — manual testers who can describe a workflow precisely in English but cannot write Playwright scripts. | Your team is already fluent in Playwright or Cypress — switching to plain English adds ambiguity without saving meaningful time for engineers who think in code. | Playwright for complex integration scenarios and API contract tests that exceed what plain English can express cleanly. |
| Your UI releases frequently and you are spending significant time maintaining brittle XPath or CSS selectors — the AI self-healing recovers automatically from minor DOM changes. | You need detailed failure diagnostics — testRigor's debugging experience is limited compared to tools like Playwright, which give you full stack traces, video, and trace viewer. | Postman or REST Assured for deep API regression suites where plain-English API commands feel imprecise. |
| You need coverage across web, mobile web, and native mobile from a single set of tests — testRigor's cross-platform engine avoids duplicating test logic across frameworks. | Budget is tight — at ~$300/month minimum, testRigor is one of the more expensive no-code options; Testsigma or Robot Framework offer similar plain-language authoring at lower cost. | Jira + Zephyr for test management and traceability; testRigor's native reporting alone is insufficient for regulated environments. |
| Your organisation is actively upskilling manual testers into automation roles — plain English syntax shortens the ramp-up time from weeks to days and builds confidence early. | Your application requires on-premise test execution for compliance or data sovereignty reasons — testRigor is primarily SaaS and its on-premise option is limited and costly. | mabl as a complementary tool where recorder-based authoring suits exploratory-to-automated conversion better than writing from scratch in plain English. |
What I would do
Practitioner judgment on tool adoption, team onboarding, and when to swap.
If…
I were a QA lead at Foodstuffs NZ — which runs a large, high-throughput e-commerce platform across PAK'nSAVE, New World, and Four Square — with a team of five experienced manual testers and no automation engineer on the books…
I would…
Pilot testRigor on the checkout and loyalty flows first — those are the highest-value, highest-churn areas where UI changes are frequent and a broken test suite is costly. I would not attempt to automate the full regression suite immediately. Instead, spend the first month building 30–40 tests covering the happy paths, measure maintenance overhead after two sprint cycles, and use that data to justify the ongoing licence cost to leadership. If the team's test coverage is still below 60% of critical paths after three months, I would add Playwright for the backend-heavy scenarios and treat testRigor as the non-technical testers' tool, not the whole strategy.
If…
I were the test automation lead at KiwiFirst Bank, where data sovereignty is non-negotiable and the risk and compliance team has concerns about test data leaving NZ infrastructure…
I would…
Not adopt testRigor for core banking or payments testing — it is a SaaS platform and the on-premise option is not mature enough to satisfy a bank's data handling obligations. I would use Playwright with synthetic test data for those flows. However, I would consider testRigor for testing the publicly facing home loan calculator, rate comparison pages, and digital onboarding screens, where no sensitive customer data is ever entered during automated runs and the UI changes with every marketing sprint. The key discipline: always use clearly synthetic data (e.g., generated NZ bank account numbers, fake Revenue NZ numbers) and confirm with the security team which environments testRigor can reach before writing a single test.
If…
I were a senior tester at ListRight tasked with reducing the manual regression effort before each fortnightly release, with a team that includes both developers and non-technical QAs…
I would…
Split the tooling deliberately: let the developer-aligned testers own Playwright for component and integration tests that live in the same repo as the application code, and give testRigor to the non-technical QAs for end-to-end regression of the listing, search, and payments flows. Avoid the common mistake of trying to unify everything on one platform — testRigor is not a good fit for testers who want to assert on API responses or query the database to verify state. Let each tool do what it is actually good at, review the combined coverage report fortnightly, and replace any testRigor test that fails more than twice in a row with a Playwright equivalent written by a developer.
The bottom line: testRigor earns its place when the bottleneck is authoring speed for non-coders, not when the bottleneck is test reliability or debugging depth — buy it for your business analysts, not your automation engineers.
Interview questions
Questions you are likely to get if you list testRigor on your CV — with what interviewers are really testing for.
Why would you choose testRigor over Playwright for a regression suite — and what are the trade-offs you are accepting?
What they’re really testing: Whether you understand that no-code tools are a deliberate trade-off, not a free upgrade — and whether you can articulate the costs honestly rather than just selling the benefits.
Strong answer covers: testRigor removes the coding barrier for non-technical testers but sacrifices debugging depth (no stack traces, no trace viewer); self-healing is resilient to DOM changes but can silently mask real bugs if the AI recovers from a genuine regression; and at ~$300/month minimum it is expensive compared to open-source Playwright — a strong answer names the team profile and budget context that tips the decision.
When would you use testRigor over Testsigma, given both support plain-English test authoring?
What they’re really testing: Whether you have done genuine tool evaluation or just listed testRigor because it sounded impressive — interviewers want to see comparative thinking.
Strong answer covers: testRigor has stronger AI element detection and broader native-mobile support; Testsigma tends to be more affordable and offers better on-premise deployment options for compliance-sensitive NZ sectors like banking or government; choose testRigor when cross-platform coverage (web + mobile from one test) is the priority and budget allows.
You are joining CloudBooks’s QA team and they want to automate regression for the invoicing and payroll flows. The team has five manual testers with no scripting background. How would you approach rolling out testRigor?
What they’re really testing: Practical rollout judgment — can you plan a pilot, set realistic expectations, and manage the change from manual to automated without burning the team out or blowing the budget.
Strong answer covers: Start with a 4-week pilot on the highest-value, highest-change flow (e.g., invoice creation and GST calculation) to prove ROI before committing the full licence; train testers to write tests immediately after manual sessions while steps are fresh; establish a rule that any test failing more than twice in a row gets reviewed rather than re-run — CloudBooks’s payroll data sensitivity means silent self-healing bypassing a real defect is unacceptable.
Your testRigor tests pass consistently in the testRigor cloud but fail when triggered from your CI pipeline. How do you diagnose that?
What they’re really testing: Whether you can debug a tool that deliberately abstracts away the execution layer — a genuine skill gap for testers used to open-source tooling with full logs.
Strong answer covers: Check whether the CI trigger is hitting the correct environment URL and whether the test account credentials are available to the CI runner; compare the testRigor execution video and step-level logs between a passing cloud run and a failing CI-triggered run; common causes include environment variables not set in the CI context, network firewall rules blocking the testRigor agent from reaching the test environment, and timing differences caused by slower CI runners — fix by parameterising environment config and adding explicit wait steps rather than relying on implicit AI timing.
How would you structure a testRigor test suite to scale from 50 tests to 500 without the suite becoming unmaintainable?
What they’re really testing: Architecture thinking beyond getting tests to run — can you anticipate maintenance debt, reuse patterns, and governance at scale before they become problems.
Strong answer covers: Use testRigor’s reusable test steps (sub-tests) for repeated flows like login, navigation, and logout rather than duplicating plain-English steps across hundreds of tests; tag tests by feature area, priority tier, and environment so CI only runs the relevant subset on each trigger; establish a naming convention and ownership model early — without it, 500 plain-English tests become as hard to navigate as a badly organised spreadsheet; schedule a monthly suite review to retire tests for deprecated features.