Expanding Test Coverage, the way of AI/Codeless Platforms

First, let’s start by refreshing a little bit the concept test coverage.

Software development becomes more systematic day by day, and companies look for ways to achieve testing completion criteria. There are several criteria for this matter; however, among those, test coverage is of especial value.

Test coverage can be defined as the measure which determines what extension of the tested software’s code is exercised and how much of it is actually covered by our tests. A well strategized test coverage ensures the most critical modules or components of our application code are checked for bugs, hopefully in early stages of the development process so as to make things as much as possible test-driven and ensure we’re building the right product while building the product right.

There is no myth about the reaching of 100% test coverage; it’s long been proven to be impossible, but if it was possible, it would not completely avoid bugs to appear. Conversely, if a minor fraction of the code was covered by our test cases, it wouldn’t be necessarily wrong, because test coverage, in general, will be relative to the priorities we may have at a certain context and time, provided the proper risk analyses.

There are, for example, circumstances and contexts in which we might prioritize testing the UI, and others in which we might decide to emphasize on performance, and others in which might be more appropriate to focus on back end components and/or data integrity, etc.

Whatever it is the priority we decide to establish for our testing coverage, the importance should be placed in maximizing it. Expanding it as best as possible.

As Tibor Uittenbogaard from One Shoe states: “Tests should evolve to provide more and more quality and quality assurance along the way. For every new insight, bug or feature, the developers should ask themselves if an automated test can prevent future bugs, and they should assess if a test shall have a level of added value that is equal to- or greater than the cost (time) of implementing the test.”.

With Autify, manual testers and QA Engineers can build more tests faster, increase coverage and reduce maintenance. Manual testers can be added to automation testing and help increase the release cycle. Automate once, test forever, do more with less, faster.

You can run Test Scenarios on multiple browsers simultaneously. There is no need to prepare a Scenario for each browser. Maximize test frequency and coverage with the same amount of resources.

We have a long-term roadmap traced in order to expand Autify’s capabilities and product lineup, which consists in three phases:

1. Increase automation coverage. We will provide a product that can automate a greater proportion of test cases that are currently being tested manually.
2. Increase overall test coverage. Since test coverage varies from person to person, we’ll provide a product that increases the test coverage along with the automation percentage.
3. Eliminate test phase. Automate tests at the initial stage of development, driving it by testing. Once all tests pass, the software can be released without a separate test phase.

Artificial Intelligence can no doubt help reduce costs and save time, in spite of frequent code changes. Maintenance headaches can be exiled to the past, while our test automation experiences dramatic improvements and time to market decreases in an absolutely notable way.

You can see our client’s success stories here: https://nocode.autify.com/why-autify

We sincerely encourage you to request for our Free Trial and our Demo for both Web and Mobile products.