Let’s get a decade or two years back in time. It was an era where people had limited access to technology and the internet.
But the gradual evolution of the digital world has made mobile devices, the internet, and technology an essential part of our day-to-day lives. From day-to-day activities to business transactions, the use of business websites, software, and applications streamlined everything making way for a digital world.
Nevertheless, the massive surge in demand for tech solutions possessed a major concern for quality assurance services. To meet the expanding needs and land quickly in the market, development companies started resisting software testing services. However, to cope with sustainability, scalability, and reliability goals, the QA industry had to shift towards more advanced testing procedures like Automation.
Understanding the Need for Automation
The concept of test automation has evolved dynamically with the introduction of technologies like Artificial Intelligence and Machine Learning. Especially, when both AI and ML have taken a huge space in our routine operations, right from bots to Alexa, these solutions have huge potential to complement the competitive software landscape that involves massive demands and scope for quality.
Coming to the facts, greater exposure to technology has made customers more intolerant than ever when it comes to product defects. Since it can make them switch to alternatives, gaining the loyalty of customers entirely depends on the product quality and services.
Though ever-increasing competition has brought great exposure to the world in the form of innovations, it has created a gap in quality assurance operations. Even if the development companies have found collaborations with QA service providers as a way out, the massive demand and competition have made the QA process more complex.
Here comes the role of Automation. AI & ML in test automation has all the potential to meet the requirements related to speed and quality at the same time. From creating end-to-end testing strategies to the development of test cases from scratch, automation can drive all the efficiency and quality required to get coverage on all high-risk test scenarios.
As AI and ML based test automation is the new buzz in the QA industry, this blog will aim at highlighting all the areas where AI and ML could help advance Automation on QA and software testing operations.
Test Automation Using AI and ML: The Applications
Since the QA industry is expanding at a very fast pace, improving results on traditional test automation needs access to smarter test automation tools. Here is how test automation using machine learning and AI could help improve the test automation process and expand the test coverage with reduced efforts.
Since test script maintenance is one of the trickiest aspects of test automation, the self-healing technique could help work on areas where scripts break due to changes in object property. This is all about using a dynamic location strategy where AI works on identifying name, ID, CSS, and other information that undergoes changes and requires fixes. The process can even aid the shift-left testing approach complementing the agile methodology and delivering necessary efficiency to meet productivity goals.
A quick example of the same can be a UI identifier that allows automated rectification of any changes made to object identifiers on the HTML page. The AI engine responsible for the operation works by locating the elements despite the attribute iterations made to the source code. Such a technique could help automation QA testers to save plenty of time to identify and update the changes that might affect the UI.
Usually, the AI-based test platforms work by an AI engine that figures out any changes made to object property that is likely to break the project. The process, therefore, ensures effortless implementation of test cases by processing any changes through dynamic location and fixes.
One of the most tiring parts of automation testing is developing automation test scripts for advanced programming languages like Python, or Ruby. The process demands high efficiency and skills to meet the initial requirements of the project but has the capability to reduce the time for test script generation to 50 per cent. Moreover, adding AI and ML technology into the process makes the test script writing even more streamlined.
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One of the common examples of test script generation with automation and AI is the use of selenium to write test scripts based on manual test cases. The tool works by reading the test scripts and uses NLP to comprehend the user intention in form of test scripts. More importantly, the modern-day testing tools do not require engineers to write even a single code, reducing the design time and efforts by up to 80 percent. This process is defined as touchless automation testing.
The test automation service providers that work on continuous testing based on Agile methodology and DevOps prefer to opt for end-to-end testing. In other words, the test process involves an integrated approach to API, Accessibility, Integration, Unit testing, and other testing processes within the development lifecycle.
The approach thus produces a large volume of test data. Though test executives may get overwhelmed with such a volume of data, Machine learning technology can process all the data to visualize unstable test cases, simplifying the development and QA operations. ML can be used to slice, dice, and analyze the information to quantify risks, create reading patterns, and advance decision-making.
For instance, testers can use data to identify which task in CI/CD pipeline must be prioritized or recognize any platform that contains the most bugs. Some of the most common implementations of AI and ML for test data utilization include Application element locator’s brittleness, Platform-specific defects, Recurring patterns in test failures, Security holes, and Test Impact Analysis.
- Automated Visual Validation/Image-based Testing
The next potential application of AI and ML in test automation is Automated visual validation or image-based testing. Also known as user interface testing, visual testing works by processing the UI of the web to deliver the output as intended for the end-user.
The process is usually mistaken for a more traditional form of UI and functionality testing. However, it is a difficult process to automate and is worked well through AI and ML testing. ML-based visual validation works by allowing testers to identify elements that have chances of being skipped with manual testing.
The process could aid huge help to the automation testing services as it can detect visual bugs with much greater efficiency. Also, technology could help in validating the visual correctness of the application without any implicit input made to the system manually.
Last but not least, the spidering method is one of the most used techniques adopted by automation testers to work on test cases. The spidering AI testing process needs testers to use advanced AI/ML tools that can crawl through a web application or software to assist in test automation.
These tools collect data by taking screenshots of the application data and HTML codes while measuring load. The dataset built is then used to train the ML model to check the product under test to deliver expected output and behaviour. In the end, the tool compared the observations made to flag any potential bugs which can further be checked by a tester that has required domain knowledge to validate and fix the issue.
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Though most development firms and QA service providers are leaning on AI and ML solutions, leveraging the true potential of automation with AI and ML needs a more strategic approach.
However, when you have a team of experts who understand the complex data structures and have roots in AI and ML to simplify the observations, decision-making becomes much easier. More importantly, partnering with the right automated software testing company can help you yield all the efficiency and effectiveness required to enhance the product outcomes.
Nevertheless, Artificial intelligence and Machine Learning in test automation are the future of the digital landscape and therefore companies that are reluctant or uninformed of implementing these techniques into their test process are likely to fall back on the quality of operations and services.
Reach our experts at BugRaptors, a leading name in software testing and quality assurance that can help you get over all the QA inefficiencies and help you walk towards an intelligent tech landscape.
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