Using AI to Create Model Based Testing

There’s a new hero in this AI world, and that is Model Based Testing (MBT)! And it’s the perfect time to understand why – according to the CTR survey, respondents are saying that they foresee extensive use of model-based testing in the upcoming year.

Testing the code…A battle against time!

Is your application stable after undergoing modifications? How often do your tests fail due to developers making changes to your application?

With releases going out daily, and at times hourly or almost every minute, the testing teams are always in a battle against time for writing test cases. The number of changes in the testing code is proportional to the changes made by the developer in the application. A major challenge for the testing teams is to align with the speed of the Agile development team where user and business requirements may change often. 
Although classic test automation may be a solution to this problem, it comes with its own challenges of being time consuming, poor test requirements capture, limited and incomplete test coverage, and maintenance of automated tests.

Do you want to get smarter with automation solutions to complete your testing cycles, decrease your defects and gain a competitive advantage?

If yes, then your first step would be to understand the use of Model Based Testing in creating test cases and automation scripts.

So what is Model based Testing and why is it so important?

Model-based testing is an application of model-based design where test cases are automatically generated, executed and checked based on formal specifications of the system under test. No human intervention is required to write and maintain the test cases.

The models can be used to:

• Represent the desired behavior of a system under test (SUT)
• Generate automatic test cases
• Represent how we expect the system to behave under test

Advantages of Model based testing

• Helps identify specification and design bugs even before the code exists
• Easier to maintain test cases and test suites.
• Reduces recurring test case maintenance costs 
• Improved Test Coverage 
• Ability to execute different tests on multiple machines to test how the system works 
• Early defect identification
• Increase in defects volume.
• Ensures enhanced customer experience due to quality testing approach

Pitfalls of the Current Model Based Testing systems

Model Based Testing is not a tool, it’s a cultural shift. The deployment of model based testing into an organization requires considerable effort. Some of the known challenges are:

• Steep Learning curve for testers due to the need of understanding modeling and coding skills  
• Testers operate in a severe time crunch.
• The test metrics do not map easily onto test generation.
• Difficulty in understanding the model as its needs deep knowledge of the application architecture

How AI addresses the problems with Model Based testing

With our systems getting more complex and smart, the expected outcomes for each user input and action differs. These can no longer be tested using the traditional testing techniques. Given the changing environment, we need something smarter to test these AI-ML powered systems. 
According to the Continuous Testing Report, Model-based testing is a crucial enabler of continuous testing.

Creating the Test Cases using AI

Test cases are integral to test documentation and aid in requirement mapping, future referencing, and form a base repository for test automation. 

In the traditional model of testing, testers create the test cases based on the requirements, user stories, acceptance criteria and identified quality issues and bug reports. The test cases are then updated based on the issues found during the testing cycle and bugs reported by users when the system is in production. However, at times the requirements are ambiguous, or they keep changing frequently, resulting in poor quality test cases. 

AI powered model-based testing approach addresses these challenges by automatically generating the test cases and scripts from a model.

Automatic Test Case Generation is the process of identifying and creating test cases for testing the adequacy of new or updated versions of software applications without the need for any human intervention.

The AI based testing system will:

1. Learn the software 
   1. The model, generally called the test model, represents the expected behavior of the system under test (SUT). 
2. Generate the model 
   1. This is a fully automated process that generates the required number of test cases from the test model.
   2. The tests are updated automatically using the Self-Healing mechanism if any new changes are introduced to the model. 
3. Recommend it to the Tester in an easy to understand format  

How Webomates does it

In today’s global competition, the ability to inspect the product quality comprehensively and reliably is a key success factor for organizations. 

Webomates’ powerful, patented CQ Portal uses advanced AI and ML algorithms and deep learning to produce actionable results from multivariate problems. With its intelligent speed, it can produce up to 2000 Test cases in 4 weeks. It runs the tests and provides the user with pass/fail reports, triages the pass/fail results and identifies and creates defects for the user of the platform to review.

Webomates CQ platform generates a package composed of:

1. Test strategy for an application
2. Test case that is human readable
3. Test model that helps in regenerating cases for self-healing
4. Test script that can be executed on multiple automation systems
5. Test script that can be executed on multiple crowdsource systems
6. Test script that can be used by the Webomates AI assisted manual testing system

Benefits

AI powered Automated Test Case Generation results in:

• Self-Healing capability – As the full regressions and modular tests include healing of the test cases and test scripts for modified features, the Test Package is updated with the new behavior of the software release. 
• Higher Test Coverage – Applying AI and ML increases the depth and scope of the tests 
• Reusability of Test Suite – Once a test suite is defined, it can be easily replicated for various other use cases.
• Real time analysis – Instant reporting helps to reduce the feedback loop between developers and testers

Eliminates human error – Manual testing is prone to errors. Automated tests can execute the steps precisely and repeatedly leaving no room for any human error, especially for complex scenarios.

Conclusion

It’s the era of smart testing.

The complexity of applications across domains has significantly increased over the years, making software testing critical to ensure that the system, as a whole, meets both functional and nonfunctional requirements. The model based testing approach is a highly promising approach to develop and deploy software releases, faster and smarter!

With a perfect amalgamation of Agile, DevOps, Continuous Testing, patented AI Defect Predictor tool and a test automation framework, Webomates helps you in realizing the true business value and also empowers the organizations in providing value to the customer.
If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo, or reach out to us at info@webomates.com.

Tags: Intelligent Test Automation, MBT Technique, MBT Testing, Model Based Testing