Cognitive QA: How it Aids Quality Assurance

In the last few years, we have seen the rise of Artificial Intelligence and Machine Learning in various industries. For building such intelligent and complex systems, quality becomes a vital deciding factor behind its success. Most product companies know that to build high-quality products, Quality Engineering has to become an integral part of the product development process.

The onus is always on delivering high-quality products and achieving faster time to market. The adoption of automation across various facets of testing has enabled rigorous testing at a faster pace. However, with the passage of time testing methods do need to evolve in order to aid superior product development and improved speed. With the advent of AI-driven applications, apart from self-driving cars, virtual assistants, drones, etc. we can leverage AI and ML itself to test products and application software. The application of AI and ML as part of the QA process is Cognitive QA.

What is Cognitive QA?

If we are looking for a definition to understand Cognitive QA, then we can define it by saying that, it is a process or an approach of applying artificial cognitive methods to achieve automation at a rapid pace. Cognitive QA leverages AI and ML in testing by extracting patterns in existing datasets. AI has the ability to process large amounts of data, thus providing useful insights in a short span of time.

Cognitive QA brings automation and collaboration on the same page. It gathers actionable insights from various channels and uses them to enable faster business decisions. The greatest advantage of Cognitive QA is that it can give a lot of assistance in deciding how to go about the process. It helps in identifying the most effective test-sets in a regression suite. It can help in evaluating which test cases are optimal for automation. Apart from this, it can assist in creating test cases, and eventually testing the system.

How Does Cognitive QA Help?

• Data along with analytics is key to making decisions in a business. A QA dashboard that gives you real-time reliable data can help in improving the quality process. The improvements can be made to the current or future releases, which will ensure better quality and user experience. You can also source customer feedback and address customer challenges through future releases.
• Sometimes testers may have confusion on what to test, and how many test cases are required to get maximum coverage. Unless your testing process is focused, you may not get maximum output out of it. The process becomes objective when algorithms are used to analyze and understand data to identify test cases that may be repeated in other testing sets. You can also eliminate test cases that cover recurring issues. This will help in making the testing efforts more streamlined and focused.
• Cognitive QA can help the tester by selecting the scenarios automatically that provide the best RoI on test automation and this is why automated generated test data is required. It can also help in automatic generation and selection of test cases based on the aspects that are being tested.
• If the target is continuous improvement, then we require insights and analytics in order to identify issues and risks. Businesses can use tools that aid continuous monitoring, predictive analysis based on usual patterns, and the use of ML to define strategy and test coverage.

Advantages of Cognitive QA

• Cognitive QA helps you in achieving maximum code coverage. Due to the presence of ML, testing becomes more accurate and it results in improved customer experience.
• The cost of fixing bugs after release can be expensive. Cognitive QA can help in identifying bugs early on and solve them, thus saving expensive post-release bug fixes.
• It facilitates qualitative testing, not just quantity, and aligns itself with business inputs to enable better decision making.
• Automation is an integral part of Cognitive QA and effective automation always results in reduced test cycles. This in-turn aids faster time to market.
• The progress also needs to be tracked while the project is on. In the Cognitive QA approach, you can have effective tracking to measure the progress of your process.

The Cognitive QA approach can enable you to test your products and applications with more efficiency and intelligence than most quality processes. eInfochips offers Cognitive QA services that use advanced machine learning algorithms and have been delivering top-notch quality services for various market leaders in different industry segments. To know more, contact us today.