Autonomous Testing and its Applications
Autonomous testing is the ability to build tests autonomously without users’ interference. Accurately testing applications is a time-consuming method, and autonomous testing holds the promise of significantly decreasing that work.
I’ve spent the past couple of years trying several strategies for autonomous testing.
A Brief History on Quality
The testing industry hasn’t really developed much since the Mercury days. Too much time is spent on QA. The penetration of automation extended only by a few points, and coverage is still insufficient.
QA is normally a combination of some unit testing and a lot of manual QA. Proper coverage comes at a high price, and most organizations just accept the thought that bugs will move to production.
Businesses lack a proper QA environment (e.g., they can’t test on live data and it’s hard to build a staging environment).
They can’t allow proper test data and initialize the environment. After all, automated tests allow a particular state, execute certain actions, and demand certain results.
There’s a high maintenance overhead, particularly due to the requirement to update the tests with new functionalities to test user experience end to end.
The Challenges of Deploying Autonomous Testing
There are many challenges connected with deploying autonomous testing. Below are a few things you should bring into consideration.
Goals of Testing
If you give the same situation to 10 SDETs, it’s likely that each will perform this scenario differently and assert different things. If you imagine scenarios autonomously, which one will you choose, and will it fit what the QA team dedicated to test?
Sometimes you want to test negative scenarios, such as a user not being ready to perform particular actions before certain requirements are met. Another common instance is testing password validation (e.g., proper error messages for passwords that don’t comply with the policy). How can autonomous testing test such situations?
User Profiles and Permissions
Many apps customize their experience to various user profiles (basic vs. pro, standard vs. administrator). We operate quite a few scenarios validating that some profiles have permissions only to particular functionalities.
While autonomous testing can maybe test the application’s functionality, it doesn’t understand the expected outcome and doesn’t recognize what to assert for. A typical example is the checkout method in a shopping app. An SDET would correctly assert that the total amount matches the prices and quantities of the items bought. How would an autonomous testing tool understand to assert for that?
Functional testing is just a component of quality assurance. User experience testing involves testing the responsiveness of the app below various network conditions, disruption testing (e.g., the app works in the background), personalization to location, language, and so on.
Autonomous Testing Has Value
Organizations that take quality sincerely will have a list of scenarios they work over and test for before each release. CTOs, VPs of engineering, and QA leaders we’ve spoken to need to understand that those scenarios are being tested as opposed to some black box that fundamentally does exploratory testing.
A New Way of Autonomous Testing
The proof of autonomous testing is clear: immediate coverage with no trouble. I’ve come to the understanding that building tests autonomously might not be the correct objective. Tests, by nature, look into corner cases and need an understanding of what to assume. Two QA engineers can take the same scenario and test it uniquely. Moreover, the more organizations I operate with and the more tests I see, the less confident I am that I, an intelligent human, would have come up with the situations myself.
Let’s redefine autonomous testing as a training model of the application that can do these three things:
- Help the QA engineer build tests by predicting what they need to test with minimal information.
- Ensure there’s some correspondence between the model and the tests so that they can autonomously update each other.
Test scenarios and test cases must be generated automatically by the tools, ensuring comprehensive coverage from an application perspective. Any modifications in the application must be recognized and self-healed along with the study of affected areas due to the change. Intelligent test selection simultaneously with impact analysis must be implemented to bring the appropriate amount of balance between time and amount of testing to be performed. Also, the user dashboard must be interactive with all comprehensive reports. A tool that consists of the features will be able to efficiently manage the Cost, Time, and Quality equilibrium.
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