Benefits of Intelligent Automationation
Artificial Intelligence is a technique that enables a computer system to exhibit cognitive abilities and emulate human behavior based on pattern recognition, analysis, and learning derived from available data with the aid of predetermined rules and algorithms.
Machine learning and deep learning are two terms that are often used every time Artificial intelligence is discussed. People tend to use these interchangeably, however, there is a fundamental difference between them.
Understanding the fundamental difference between AI, ML, and DL fundamental difference between AI, ML, and DL Artificial intelligence is the superset of machine learning and deep learning.
Machine learning is a subset of AI which aids computer systems in learning and decision making without explicit human intervention. It works on pattern recognition technology and works with predefined algorithms to understand, learn, process, infer and predict, based on past data and new information. Its prime focus is to aid in decision-making. AI improves as ML improves.
Deep Learning is a subset of machine learning, also called scalable machine learning. It helps machine learning algorithms by extracting zeta bytes of unstructured and unprocessed data from data sets.
What makes intelligent automation important in software testing Test automation promised to revolutionize the world of testing when it was first perceived and implemented. It delivered on that promise by improving overall testing speed and results. However, as technologies and processes further evolved, there was a need for improving the testing process too.
If you want to understand the journey of the testing process from manual to AI era, then read our blog “Evolution of software testing”.
Automation eased the testing load, but it could not “think”. For instance, test automation can execute thousands of test cases and provide test results, but human intervention is needed when it comes to deciding which tests to run. Adding the dimension of intelligence can add analysis and decision-making capability to test automation.
Intelligent automation works on data like test results, testing metrics, test coverage analysis, etc., which can be extracted and utilized by AI / ML algorithms to identify and implement an improved test strategy for efficient testing.
As per the Gartner study, “By 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets”
Let us explore further how intelligently automating the testing process helps in improving overall QA operations.
test reliability with improved accuracy Higher level of test reliability with improved accuracy In the era of DevOps with frequent and shorter development cycles, continuous testing is conducted for every minor/major change or a new feature. While test automation has helped a lot in reducing the testing burden, adding AI to automation can enhance the overall testing process, since it keeps evolving based on new information and analysis of past data. It also aids the teams in identifying the tests for better test coverage. With intelligent automation tools doing a large portion of recurring tedious tasks, the developers and testers can focus on other aspects like exploratory testing and finding better automation solutions. Improved risk profiling and mitigation with enhanced test result analysis Intelligent automation renders the ability of risk profiling to testing. Intelligent automation and analytics help the testing and development teams to have a better insight into the impact of code changes and risks associated with those changes. Appropriate actions can be taken based on these insights and issues can be intercepted much earlier Deeper insights in test results and predictive analysis Test reports and analysis are critical processes of software testing. It helps the teams in understanding the loopholes in their current test strategy and consequently aids them to define better strategies for the next test cycle. AI-infused tools can analyze and understand the test results, spot the flaws and suggest the workarounds. These tools constantly learn and update their knowledge base with every test cycle, based on test result analysis and apply that knowledge to improve software testing by detecting even minor changes and predicting the test outcome. Improved defect traceability and prediction is a game-changer when it comes to optimizing the test strategies. Boosts efficiency by transforming DevOps with benefits of AI Ops and QA Ops To match pace with dynamic software testing demands, DevOps has to be augmented with the power of artificial intelligence. QA Ops have gained importance in the past few years and enabling it further by using intelligent automation will ensure faster time to market with better quality. Faster delivery with improved results Intelligent automation plays an important role in accelerating releases since it optimizes the whole testing process based on a comprehensive analysis of previous test results. Continuous testing for frequent changes can be time-consuming, but AI/ML expedites the whole process by identifying the right set of tests to be executed, thus saving a significant amount of time and resources. Read More about : Intelligent process automation
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