Benefits and Challenges of Cognitive Test Automation

Imagine having a mechanism that solves our tech issues and provides us with the desired output, and can foresee the product's future. Wow, that seems amazing! It is cognitive test automation. Computers are smarter and thanks to machine learning, artificial intelligence, big data, data science. What exactly comes in cognitive testing? It is the class of testing that leverages AI and ML, and other cognitive computing techniques.

Across various industries, cognitive testing is implemented. Telecom, banking, financial services, healthcare industries are among the list. Businesses are keen to adopt this technology because it allows businesses to stay ahead and provide quality products.

Some openly available platforms are 

1. Open AI - It researches AI and is a non-profit organisation.
2. IBM Watson - Great question-answer machine to learn natural languages
3. Google DeepMind - Neural network and deep learning to train artificial intelligence. 

Why is cognitive testing the future of QA and testing operations?

The reason being smarter products proliferate the market at record speed and thanks to integrating agile testing and DevOps. Despite this glaring discrepancy of volume and speed in testing, businesses still rely on manual testing processes, and thus, they are hampering their own growth by not using cognitive QA. 

RPA vs Cognitive Test Automation

It is a challenge for a business to choose between cognitive automation and RPA. Let's make a wise decision by understanding the difference. Both technologies support automation, but cognitive automation helps mimic human actions rather than taking action or decision like robotic or software automation. Cognitive testing brings intelligence into the overall automation framework.

For healthy decisions, scope and expectations are essential. For instance, if one does not bother to have logical action and instead replicates regressive tasks mostly because of agile nature, then execution in a defined time and environment is excellent, then RPA will serve a purpose.

If a product demands natural language processing, data mining, or any logical data processing task, then cognitive automation is the one-stop solution.

Benefits Of Cognitive Testing

Cognitive automation enables the processing of huge volumes of data in an incremental way. The data processing capabilities make it far more superior to human capabilities. There are multiple challenges that an organisation needs to address before implementing cognitive automation in its software. But along with challenges comes benefits. Let’s learn the benefits of cognitive testing. 

• Future prediction and early bug detection
• Best for agile projects as it is adaptive
• Better and faster decision-making concerning a phase of the project
• Overall product delivery time, efforts, and cost are reduced
• Efficient resource management - without human intervention, all work is done using tools
• Aid in testing prioritisation
• It helps keep the product best in class with all standards through continuous feedback in cutting-edge competition

Cognitive Testing Methodologies

• Test prioritisation
• Cognitive defect analysis
• Test coverage optimisation
• Risk-based testing and delta testing
• Selective entry-exit criteria for testing
• Self-Healing test automation
• Retire the obsolete test cases after releases and delta

Challenges Involved In Cognitive Test Automation

• High Cost
• Domain Training
• Wrong or undesired results due to lack of clarity in vision
• Complexity and adaptability

How To Prepare For Cognitive Test Automation?

1. Invest early - build the knowledge and get basics cleared
2. Know and set your goals - your goals should align with your business goal and project delivery.
3. Think creative - bring in equations of data, design, and analyse everything.
4. Adaptability, agile methodology, and flexibility is important for success
5. Understanding limitations and how systems need to work for business.

According to the world quality report of 2017-18, 42 percent of businesses took part in a survey and believed that machine learning, self-remediation, and cognitive test automation are important emerging techniques for increasing outcomes and returns from test automation initiatives. 

Four Key Elements to Successful Cognitive QA

1. Quality QA dashboard

The starting point of any process involves data which includes intelligent testing or analytics. A business should rely on its accuracy and validity and put faith in data prediction and applications' quality. The most important source of insights to aid decision-making is a quality dashboard that incorporates real-time information such as product incidents, positive and negative customer feedback, future release readiness, etc.

2. Optimising for test automation

Deciding what to test and how many test cases are required is a contentious and subjective process based on human bias and emotional decision-making.

We can achieve the most relevant test result using algorithms to optimise test sets. When algorithms are used for comprehensive analysis, the decision-making process becomes more objective. It identifies test cases that flag defects already pinpointed by other test sets. 

3. Intelligent self-running automation

What to test and how much to automate these are the questions on which successful cognitive QA is based. It involves an automatic selection of scenarios that provide a return on investment from automation. It completely depends on the tested feature, i.e. automated generation of required test data and automated generation and selection of test cases.

4. Continuous monitoring

When businesses invest in tools, they can derive definitive inferences, valuable insights, and observable patterns to access real-life risks and issues drawn from the data. By investing in tools, a business can benefit itself by continuous monitoring, predictive analysis, and self-adaptive machine learning and can define test strategy and coverage.

Conclusion 

We hope the above points proved fruitful, and after checking massive benefits, it is sure that the implementation of cognitive test automation is key to win. Imagine having a mechanism that yields desired output and also foresees the future of the product. Yes, cognitive test automation analyse and fix issues by itself. Businesses can stand in the market, can ensure better ROI, low cost, speed, and enhanced customer support.