If these three are on your shortlist, you have already decided something important: you are not writing Appium scripts any more. All three removed code from mobile test authoring, and all three did it credibly.
So the interesting question is not which one removed code best. It is what else each one removed, because that is where they diverge, and it is the difference you feel six months in rather than during a demo.
All three removed the code. Only one removed the separate tooling.
Sofy’s answer is the broadest: describe a flow in plain English, and agents generate, execute and maintain it across real devices, while returning functional, performance and security signal from that same run. Not three tools, three schedules and three sets of results to reconcile. One execution.
This piece covers what Sofy delivers, the architectural difference between the three approaches, and where Drizz and testRigor genuinely fit.
What Sofy Delivers for Mobile Testing
One prompt to a running test
Describe the scenario the way you would explain it to a colleague, add an item to the cart, apply a promo code, complete checkout with Apple Pay, and the agent discovers the UI, builds the steps and executes on real devices. There is no recording session, no flow to assemble, no script to review. The gap between “we should test that” and “that is tested” is one sentence.
Three signal types from one execution
This is the part that genuinely differs from the other two. When a Sofy agent walks a checkout flow, it is simultaneously capturing functional correctness, performance data, app launch time, screen render, API latency, and security signal mobile app security testing such as insecure local storage, exposed endpoints and weak session handling.
Most teams currently get those three things from three tools, on three cadences, producing three reports that nobody reconciles. Collapsing them into one run does not just save license cost; it means the performance regression and the functional regression are observed on the same build, on the same device, in the same session.
Proactive adaptation, not repair after failure
When the UI changes, Sofy’s agents adjust their approach before a test would have failed, rather than failing first and healing afterwards. The practical difference shows up on release day: reactive systems show you a red build that later turns green, which still costs a CI cycle and an alert. Proactive adaptation frequently shows green throughout.
Real devices, and the frameworks people actually ship
Execution runs on real hardware across iOS and Android, covering native, hybrid, React Native and Flutter. Emulators are fine for early functional feedback and genuinely poor at reproducing hardware behavior, sensor input, network transitions and performance under memory pressure, which is most of what breaks in production.
Mobile is not an isolated product
Sofy covers web and enterprise ERP on the same platform, with module-level depth on SAP and Dynamics 365. For a team whose mobile app is a front end onto an ERP backend, a field service app, a warehouse scanner, an approvals app that means the mobile flow and the transaction it creates can be validated as one journey rather than two disconnected test estates.
Pipeline-native
GitHub Actions, Azure DevOps, Jenkins, CircleCI and Bitrise integration, with results surfacing where the team already works rather than in a separate dashboard someone has to remember to open.
Three Ways to Remove Code
The three platforms take genuinely different routes to the same starting promise, and the route determines the ceiling.
| Sofy | Drizz | testRigor | |
| Approach | Agent-native, agents generate, run and adapt | Vision AI, identifies elements visually | Plain-English command vocabulary |
| Authoring | Natural language description | Plain English | Plain English |
| Element identification | Contextual, multi-signal | Visual and contextual | No locators by design |
| Adaptation | Proactive, before failure | Re-identifies after change | Architecture avoids the failure class |
| Scope | Mobile, web, ERP | Mobile only | Mobile, web, desktop, API |
| Signal per run | Functional + performance + security | Functional | Functional |
Two rows carry most of the decision. **Scope** determines whether this tool covers your estate or one slice of it. **Signal per run** determines whether you are buying one tool or the first of three.
Where Drizz Fits
Drizz is mobile-only and built around Vision AI, identifying UI elements the way a person does, visually and contextually, rather than through selectors, XPath or accessibility IDs. That architectural choice removes locator drift as a failure class, which is the single largest source of mobile test flakiness.
Drizz reports step-level accuracy of 97–98% and roughly 5% flakiness against around 15% for traditional Appium-based setups. Those are the company’s own published figures rather than independent benchmarks, but the underlying claim is architecturally sound, you cannot break a selector you never used.
Where it makes sense: mobile-first teams with fast-changing UIs, heavy dynamic layouts and frequent redesigns, whose primary pain is flaky tests and who do not need web or backend coverage from the same platform.
Where Sofy differs: scope and signal. Drizz solves mobile functional flakiness well. It does not extend to web or ERP, and it does not return performance or security data from the same run, so a team with a broader estate is buying one component of a stack rather than the stack.
Where testRigor Fits
testRigor takes plain English furthest as a design principle: no code, no locators, no selectors, with tests expressed as instructions a non-technical person could write. It also converts recorded sessions and imported manual test cases into plain-English automated tests, which matters for teams sitting on a large manual regression library.
Its breadth is real, web, mobile, desktop and API from one platform. The trade-off, noted consistently in practitioner feedback, is that the plain-English model rests on a defined command vocabulary. Teams pushing into highly complex or non-standard scenarios sometimes reach the edges of what that vocabulary expresses gracefully.
Where it makes sense: manual-QA-heavy organizations wanting the lowest possible technical barrier, particularly those converting an existing manual test library rather than starting fresh.
Where Sofy differs: an agent reasoning about intent is not bounded by a command set the way a vocabulary-driven parser is, and testRigor’s breadth across four surfaces comes at the cost of module-level depth on any one of them. For mobile specifically, Sofy adds real-device performance and security signal that testRigor does not return.
The Device Matrix Question
One thing worth checking before any of these tools: whether your device matrix reflects your users or your assumptions.
Android version concentration makes this harder than it looks. Where two iOS versions cover the large majority of active iPhones, Android’s most-used version sits in the low-to-mid-twenties as a percentage, you can check current distribution on
StatCounter’s Android version share tracker, which means reaching comparable coverage takes roughly seven major versions rather than two. Layer OEM interface customizations on top and the number of meaningfully distinct configurations grows again.
The practical consequence for tool selection: a platform that reduces per-configuration maintenance cost lets you widen the matrix, while one that doesn’t caps your effective coverage at whatever your team can triage. Widening the matrix first, before fixing maintenance economics, is how teams end up paying for device access they cannot sustain.
Which One to Choose
Choose Sofy if…
- You want functional, performance and security validation from a single run rather than three tools
- Your estate includes web or ERP alongside mobile, and you would rather not maintain separate test platforms
- Your mobile app front-ends an SAP or Dynamics 365 process you also need validated
- You want tests that adapt before they fail rather than healing afterwards
- You need real-device execution across native, hybrid, React Native and Flutter
Choose Drizz if…
- You are mobile-only and expect to stay that way
- Flaky tests from dynamic layouts are your single dominant pain point
- You do not need performance or security signal from the same platform
Choose testRigor if…
- You are converting a large manual test library and want the import path
- You need desktop or API coverage that Drizz does not offer
- Your scenarios are standard enough to sit comfortably inside a defined command vocabulary
An Honest Note on Scope
To be straightforward: if you are mobile-only, expect to remain mobile-only, and your single concern is flakiness on a fast-changing UI, Drizz is a focused tool built precisely for that and worth a look. Sofy’s advantage grows with the breadth of what you need covered, which is a real advantage for most teams, and a smaller one for a team testing a single app in isolation.
Frequently Asked Questions
What is the best AI mobile testing tool in 2026?
For teams needing more than functional mobile coverage, Sofy, it returns functional, performance and security signal from a single real-device run and extends to web and ERP on the same platform. Drizz is the stronger focused choice for mobile-only teams whose dominant problem is flakiness. testRigor suits organizations converting a large manual test library who also need desktop or API coverage.
How does Sofy compare to Drizz for mobile testing?
Both remove locators and both run on real devices. The differences are scope and signal: Drizz is mobile-only and returns functional results, while Sofy covers mobile, web and ERP and captures performance and security data in the same execution. Sofy also adapts proactively before a test fails rather than re-identifying elements after a change.
Is testRigor a good alternative to Sofy?
They solve overlapping problems differently. testRigor’s plain-English model has genuine breadth across web, mobile, desktop and API, and a useful import path for manual test libraries. Its vocabulary-driven approach can hit limits on highly non-standard scenarios, and it does not return performance or security signal. Sofy’s agents reason about intent rather than parsing a command set, and consolidate three test types into one run.
What does no-code mobile testing actually change?
The useful change is not the absence of code, it is the absence of locators and per-device scripts. Platforms identifying UI elements contextually rather than by resource ID or XPath survive OEM skin and layout differences that break selector-based tests, which is what allows one described scenario to run across a wide device matrix without device-specific branches to maintain.
Do any of these replace real device testing?
None should. All three execute on real devices, and that matters: emulators cannot accurately reproduce hardware behavior, biometric authentication, camera, GPS, network transitions or performance under memory pressure. Emulators remain useful for fast functional feedback during development; real devices are where pre-release confidence comes from.
The Bottom Line
Drizz and testRigor are both credible, and both solved the authoring problem properly. If mobile functional testing is the entirety of what you need, either will serve you.
The reason to choose Sofy is that mobile functional testing is rarely the entirety of what teams need. Performance and security still have to be validated. Web usually sits alongside mobile. And for a growing number of teams the mobile app is a window onto an SAP or Dynamics 365 process that also needs testing, which is three or four tools if your mobile platform only does mobile.
One description. One run. Functional, performance and security, on real devices, across the surfaces your product actually spans.r than a sampling one. And validate against the close calendar your controllers are actually working to, not the periods SAP happens to leave open.
See Three Signal Types From One Run
Describe a mobile flow in plain English and watch Sofy return functional, performance and security results from a single execution on real devices.