Role of Data Science and AI in Improving Test Automation!!

As the world is moving faster with emerging new technologies the need of improving Test automation also grew. Now when large data is required for testing within less time latest technologies such as Data science and AI can be helpful to improve Test Automation. Where data science can help in generating data from different sources AI can help in creating codeless automation based on data generated.

What is Data Science and AI?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition. Learning, observation.

Need of Data Science
Traditionally, the data that we had was mostly structured and small in size, which could be analysed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analysing and drawing meaningful insights out of it. Let’s dig deeper and see how Data Science is being used in Banking domains.

How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?

Let’s take a different scenario to understand the role of Data Science for Fraud Detection. What if customer who always makes transaction less than 500 rupees per day and suddenly his transaction spikes to 10000 a day isn’t is suspicious. In such case Data science can help in early prediction and restrict the account from making such transactions . Based on customers previous transaction history, location history, income history and data from other sources like social media will help data science to predict and act against the fraud.

How Data science and AI will improve Test automation?
Traditional testing techniques still rely on humans to source and analyse data. But let’s just say that humans are not infallible and are quite prone to making poor assumptions.
The less time there is for handling data, the greater the chance that testing will produce skewed results with overlooked bugs in the software. Before you know it, consumers will pick up on these bugs, which usually leads to frustration and undermines the brand’s reputation.
That’s why machine learning, which teaches systems to learn and apply that knowledge in the future, makes software testers come up with more accurate results than traditional testing ever could. Not to mention that the probability of error is not the only thing that gets reduced. The time needed to perform a software test and find a possible bug is also shortened, while the amount of data that needs to be handled can still increase without any strain on the testing team.

Test creation:
In Traditional Testing process human creates the test scripts on the assumption and understanding of the application but with help of Data science System can gather the real-time data from different sources identify the hidden pattern and provide new rare critical scenarios also which could help in improving the quality of Product.

With the help of data generated from Data Science Predictive analysis AI can build the test scripts Based on this AI can start creating test cases on real user data. It is smart enough to identify commonly used actions such as logging in/out of the application and cluster them into reusable components. Then it injects these newly created reusable components into our tests as well. Now, all of a sudden, we already have actual tests written by the AI based on real data, along with reusable components that can be used within other tests as well.

Test execution:
Some of the biggest obstacles keeping companies from moving forward with automation is the amount of time and effort it takes to write and execute tests with the chosen tool or framework and the availability of skilled resources to do this task. There are some AI tools that overcomes this issue. Test that use to take multiple weeks can be done now within few hours of time. This is achieved by creating reusable components, run test quickly integrate CI/CD with different grids.
As the test time is reduced the productivity of the test automation is increased and also non-technical person can perform the automation with help of AI.

Maintenance:
One of the most common problems with test automation is maintenance. For example, say we have 100 automated tests running on a daily basis to ensure the main functionalities of the application are still stable. What if the next day we come back to work and find that half of the tests have failed? We would need to spend considerable amounts of time to troubleshoot the failures and investigate what actually happened. This involves figuring out ways to fix the failures and implement the fixes. Then, we re-run the automated tests to ensure everything passes.AI can help here:

Self-maintenance:

Resultant tests are modelled and thus maintained by the combination of an exhaustive and autonomous set of data points, such as the size of element, location on a page, previously known size and location, visual configuration, XPath, CSS selector, and parent/child elements.

Self-healing tests:

Root cause analysis highlights all potential causes for test failure and provides a path for one-click updates.

Data modelling:

Selector maintenance should be eliminated by having elements identified by hundreds of data points that are rated and ranked instead of a single selector.

Conclusion
Machine learning gives testers the opportunity to better understand their customers’ needs and react faster than ever to their changing expectations. In addition, testers now also need to analyse more and more data and they are given less and less time to do that, while their margin of error decreases constantly. Data Science and AI offer a way to address these challenges. This approach is set to fill the gaps of traditional testing methods and make the entire process more efficient and relevant to the users’ needs.