Testing is an absolute essential component to accelerate the delivery of new digital applications and to ensure that these will work as expected or will break the core functionality. QA is continuously being asked to deliver more with less time and less resources and there is simply not enough time to test everything – every possible customer journey before each release.

So far testing teams have been focusing on the number of tests without considering the importance of the functionality they’re testing. If we re-assess the way we do our testing, we can achieve better coverage with much less testing. This does not mean that the quality of your applications has to suffer. In fact, advances in AI and QA can help increase the level of quality when resources and time are shrinking.

For example, what is optimal when you have limited amount of time let's say one day available for testing?

Optimal is to maximize defect detection, minimize costs by reducing the number of resources, machines, testers etc, minimize execution time by minimising the number of test cases and maximise risk coverage in a pre-defined time frame (one day). How can we achieve that?

We need to know the probability that a certain test case will detect a defect of a certain severity and the average execution time of each test case, and this can be derived and approximated from past runs. For new test cases this can be estimated based on average execution time of test cases with similar sequence of test actions. We also need to know the risk contribution of each individual test case and this can be identified when this test case is linked to a requirement and business risk.

Leveraging AI and machine learning, and by monitoring and analysing past project data and historical trends, Validata Sense.ai Risk Optimiser predicts the performance impact and recommends which test cases should be run for maximum coverage, given constraints in time, resources and defects found. It prioritises user journeys and identifies the most important test cases to be executed first, enabling you to detect bugs for the critical business areas much earlier. This way, you can take advantage of a shift-left, risk-based testing approach to mitigate the risks and test changes more efficiently.

The core of Validata Sense.ai is our AI engine which is comprised of AI and advanced machine learning algorithms, as well as a learning engine that uses Particle Swarm Optimisation (PSO) and Artificial Bee Colony (ABC) algorithms which are part of our computational intelligence technology.

It makes it safer to upgrade legacy code by showing the effect of certain upgrades and modifications of existing behaviour. It also provides a level of documentation to empower testers and developers to understand the impact of changes and make more informed decisions so further legacy challenges are prevented.

Our technology delivers precise intelligent insights to the Project or Test Manager who would want to highlight the relevant high-risk test cases, making it the best solution for an organisation that aims to create high quality test cases automatically and streamline this challenging step in the software delivery lifecycle.

Other approaches to finding bugs suffer from lack of precision creating false positives and false negatives. Our precise, ‘build for change’ technology produces real, actionable test cases rather than just creating alerts.