With the rapid changes and innovation in technology, there has been a tremendous change in software testing and delivery. Software delivery is now much faster and a more complex process than ever before. While most IT organizations have met speed and quality requirements, various loopholes still impede the automation process and make it expensive and inefficient.
Intelligent test automation fills these loopholes through its risk-based analytics feature that allows timely correction of any failure or disruption. Before diving deep into the topic, let’s understand the basic concepts of intelligent test automation and how testing tools could leverage Artificial and Machine Learning in the future.
What is Smart or Intelligent Test Automation?
Intelligent Test Automation is a blend of ML, AI processes, and traditional automation practices that make automation more stable and efficient. It uses AI algorithms that help in automatic script generation, prediction of bugs, and improving coverage. This perfect mix results in intelligent processes which have low to no error rate and high efficiency.
Features of Smart Test Automation Tool
- Scriptless test creation and execution – Smart Test Automation tools offer an intuitive interface that allows anyone with no code or limited knowledge to create and manage tests quickly.
- Rapid test creation – Allows easy automation of test cases written in plain-Text English by using Natural Language Processing (NLP)
- Reusable methods – Smart tools allow you to reuse the steps you have created before to test the scripts.
- Cloud Execution Platforms – With Integration to the Cloud Execution Platforms, test automation tools allow you to perform tests in the cloud using different browsers and multiple operating system combinations.
- Unified Platform – An intelligent test automation tool lets you automate the test cases for multiple platforms- desktop, web, mobile, tablets, etc., from one place. Thus, managing tests become easier.
- Fast Debugging – Test Automation tool allows easy integration with bug tracking tools that help you report bugs and track the status seamlessly. It directly pushes failed test cases to the Defect Tracking Tools along with the captured screenshots.
- Auto-healing – Intelligent test automation has a peculiar feature of self-healing. Whenever there is a change in function or object property, the test automatically updates itself and does not fail during execution.
- Smart Validation – Validates results with a combination of AI and a proprietary algorithm. It helps keep company and people data clean by removing invalid data, errors, or formatting issues.
- Intelligent reporting and analytics – Test automation tools have smart, robust reporting features like log analytics, risk-based, predictive analytics, etc.
- Log Analytics: Intelligently identify critical errors/failures based on the logs generated and, if necessary, log defects in the Project management tool.
- Risk-based Analytics: Detect app areas and defects that tie to company risks and setting defects as per priority.
- Predictive Analytics: Estimate key parameters and specifications of end-customer behaviors and discover app areas to concentrate on.
- Prognosis Analytics: Utilize the existing customer data and analytics data to determine how users’ demands and browsing behaviors will evolve in the future.
Benefits of Intelligent Test Automation
Some of the significant benefits that organizations can reap from Intelligent Test Automation are:
- Increase in test coverage
- Automated generation of test cases and test scripts, using machine learning
- Codeless automation
- Faster and stable UI tests
- Smart predictive analytics
- Defect prediction based on historical data and failures
- Faster Response Time, Root cause analysis, and Infrastructure optimization
- Automated test data management
- Automated test environment generation
- Automatic and smart prioritization of test cases within a reusable framework
Future Use Cases
Some of the interesting case scenarios where Natural Language Programming, Artificial Intelligence, and Machine learning can be a game-change are:
- Test Recommendation Engine: AI can help identify the test cases that should run in case of any code change in the application. AI can also determine the tests that can fix the codes and run only those scripts instead of running the entire suite. Hence, this will reduce the testing teams’ manual efforts in identifying the tests to be run for quick bug fixes.
- Learning by Observing User Patterns: AI can be leveraged by identifying the common user patterns across multiple user journeys. Based on these patterns, AI and ML engines can generate reusable components that can be used across various test scripts, thus reducing the scripting efforts of the testing team.
- Handling Response Time: The more the test runs by the user, the more the data AI collects and the steadier the tests. Based on server responses, AI can optimize the wait time for the automation scripts to accommodate different page load times in the application.
- Dynamic Locators: With the utilization of dynamic locators and multiple attributes for the same element, AI will prevent the failure of tests. Regardless of the button’s name change, it can easily choose the following best attribute to locate the element.
Future of Intelligent Test Automation
Undoubtedly, Intelligent Test Automation and Analytics will make a massive impact on the testing phase of Software development. The intelligent test automation increases the test coverage and prioritizes the focus areas, tests to be run, and detects the high-risk areas. Organizations should leverage these AI benefits to optimize the quality lifecycle of the product at every stage and deliver the project on parameters of high agility and efficiency.
Are you looking for an intelligent automation testing tool that can take your test automation journey to new heights? Connect with us to know more.