mabl is a regression test automation tool with test output visualization and performance regression for tracking the perceived speed of web apps and sites, from the company of the same name in Boston.
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Tricentis Testim
Score 8.5 out of 10
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Testim.io, from Tricentis since the February 2022 acquisition, leverages machine learning for the authoring, execution and maintenance of automated test cases. Testim uses dynamic locators and learns with every execution. It is designed to produce fast authoring and stable tests that learn, thus eliminating the need to continually maintain tests with every code change.
We ultimately selected mabl cause it most met our needs and our budget. We needed a low code UI automation test tool. We also have a suite of existing testing tools and other tools that it needed to be able to integrate with. Price was the only thing that ruled out some of the …
We haven't found a scenario yet where it hasn't been appropriate. We did have one function on our application that mabl couldn't do, but they solved it and got back to us very quickly. Our application is web-based and mabl is able to handle this very easily. We use the command line runner a lot. Being able to easily and quickly change from a cloud based run to a local run has been fantastic. Setting up flows and environments is a wonderful feature
Testim has been great at automating Frontend and integration regression testing. I do not think it would work wellTricentis Testimfor backend code testing, if it could not be done through UI. The record function is very good, and makes automating test cases much faster and more accurate. We have had great success using Testim to verify SalesForce environments, NetSuite environments, and our own JumpCloud application as well.
It provides a codeless testing environment, allowing non-technical users such as business analysts and testers to create and maintain automated tests without writing code. This democratizes test automation and accelerates test creation.
The platform leverages artificial intelligence (AI) and machine learning (ML) algorithms to analyze user interactions with the application and automatically generate test scripts. This significantly reduces the time and effort required to create test cases.
Its AI capabilities enable tests to adapt to changes in the application's user interface (UI) automatically. If UI elements change, the platform can identify and update the tests accordingly, reducing maintenance overhead.
Providing more comprehensive reporting and analytics capabilities, including customizable dashboards and insights into test execution results, could help users gain deeper insights into their test coverage and quality.
Expanding integration capabilities with a wider range of external systems, tools, and test management platforms could increase flexibility and interoperability within the testing ecosystem.
Enhancements in organizing and managing test scenarios and test suites could improve the user experience. Features such as better folder structures, tags, and hierarchical organization could make it easier to manage large test suites.
The customer support is the best I have ever experienced, both personally and in a job role. The chat anytime is a very nice option, there has never been more than a few minute wait to connect with someone, and the agents are very knowledgeable and helpful.
We ultimately selected mabl cause it most met our needs and our budget. We needed a low code UI automation test tool. We also have a suite of existing testing tools and other tools that it needed to be able to integrate with. Price was the only thing that ruled out some of the above tools, we are a small start up, and don't have a huge budget. Some tools listed didn't have the same functionality or ease of use with the record and playback. mabl works on our machines and integrates with our existing tools
We evaluated several other products, and what gave Tricentis Testim the edge was the ease of use, the customer support and the pricing. While others seemed to have a bit more complex functionality, we have been able to build almost every use case the way we would expect, with very few workarounds.
