Testing, like the software development process, has come a long way. What we now see is an improved process that implements high-end technologies. Testing has been a part of the software development lifecycle since the beginning. But instead of being an integral part of SDLC, it was conducted later at the development stage. With the rapid pace of software development, developers realized that testing must be an inclusive part of the software development lifecycle to ensure software quality.

With time, testing evolved as a powerful ninja that empowers software performance, security, and functionality. Today, software testing is not done to identify the accuracy, reliability, and quality of any computer application, software service, or product. It validates how fast you can refine a product/service to go live in the market and how promptly we can rectify mistakes and mitigate risks. And all of these must be done in a cost-efficient manner so that companies may continue to gain high ROI. It is the age of AI test automation, where less time and resources are spent on testing with advanced methods of assessing and analyzing software development performance.

Low code technology has further facilitated faster development, resulting in easy access to advanced frameworks for building the ideal product. For the FinTech sector, rapid development and test automation could not have been more useful. In this blog, we will explore more the emerging trends of AI & ML in test automation. We will also highlight how businesses leverage test automation tools to improve their QA services.

Open-source tools are the future of software development

Today, open source is the go-to option for developers working on a software product. Service providers offer a range of flexible plans to empower citizen development and managed environments for testing. Thus, organizations benefit from faster turnaround times, reduced costs, and access to a wide range of Agile, DevOps, and AI Test Automation resources. Using easily editable code, smoother functionality and minimal errors occur.

Essentially, test cycles using open-source technologies are easily accessible, faster and cheaper.

As demand goes up for engineers with software testing expertise, businesses now have dedicated teams for testing as opposed to being a function within development. Thus, SDETs (Software Development Engineers in Test) are expert coders and experts in the latest automation tools for software quality assurance. With strong technical, analytical and problem-solving skills, the role of software testers is also evolving with the upgradation of tools available.

How is AI powering organizations with test automation tools?

Since most software products/services require end-to-end AI test automation for functionality, performance, and security, the end product quality is top-notch. Firms ranging from logistics and energy to agriculture and finance are snooping around for the best ways to build, test and release products for business improvement to boost their competency.

With the software testing trend on the rise, AI-powered automation tools offer unique benefits for businesses of all sizes.

Some examples of AI test automation are listed below:

Self-healing tests: A massive share of the development time is spent on test maintenance. Using AI, engineers can build tests that heal themselves. If a test is disrupted or breaking, the AI model can study discrepancies and use historical data to fix the glitches in the testing environment. This is a huge win for tests that fail due to UI alterations but not as effective when dealing with inbuilt site logic alterations.

Visual testing: UI is the visual part of software and can use image recognition for testing. By replicating all steps involved in comparing your screen against initial tests, the AI model for visual testing can find and track changes in real-time. If any failures emerge, the system notifies you of potential breaks and errors. Yet, as UI involves a lot of changing elements like third-party advertisements and separate test cases, it may be challenging to employ visual testing for all UI-related quality assurance. Meanwhile, visual testing is quick and agile, with records of how your development has changed since testing.

Model-based testing: A novel approach to test automation, model-based testing avoids manual entries and scripts. Using AI, you can define a simple model in line with your UI functionality. The UI is viewed in layers and states, enabling users to transition between various states of the same UI for improved experience design. This method of testing opens doors to infinite possibilities. This type of AI test automation ensures that your entire UI is tested in one go. However, generating the ideal model takes a lot of effort, especially for a dynamic application that is widespread in use today.

Big data testing: Technology is powered by data and as it advances, demand for big data apps is on the rise. Yet, structured and unstructured data being generated at a massive scale required keen attention. The primary forms of testing applications built to handle big data are functionality testing, data validation, performance testing, data process validations, output validation, and others.

By building test automation tools for big data, the following benefits are assured:

  • Profit maximization
  • Data accuracy
  • Better decision making
  • Data authenticity and security
  • Improved access to crucial data

However, test automation is not limited to quality assurance though it is the prime concern for any product. Test automation has empowered better development in Agile and DevOps by supporting teams with continuous feedback, risk mitigation, faster bug detection, task automation and accuracy testing. This saves ample time, costs and resources when QA meets AI test automation.

Artificial Intelligence & Machine Learning for Test Automation

As digital transformation spans every industry and sector, shorter product release cycles, better accuracy, and incredible speed are expected. To enable increased efficiency of processes, AI and ML benefit both manual testers and test automation tools.

Using AI & ML, testing tools can learn and realize how controls work in a software by separating various elements like independent colors, text styles, sizes, arrangements, and more.

By creating test automation suites, AI/ML can enable organizations with improved technology implementation, through:

  • Bug tracking using AI/ML to accurately identify, track and eliminate errors.
  • RP testing to leverage AI/ML capabilities for high volume tasks without human intervention.
  • Record maintenance to create a library of developer resources, timeline and stages of development for now and the future.
  • Increased test coverage using advanced software testing techniques derived from data science principles.

RPA or Robotic Process Automation is the perfect example of how relevant AI and ML capabilities are to modern business. Functions like claims processing, fraud detection, identity management, demand forecasting and product development in BFSI are clear examples of how AI/ML enable improved operational control over business assets. RPA includes 4 phases – planning, development, deployment and testing, as well as maintenance and support.

RTA or Robotic Test Automation is used to conduct testing across multiple or all processes for a relatively lower volume of cases all at the same time. It provides the opportunity to test the entire case at different levels of complexity in banking environment, which cannot be easily performed by manual efforts. RTA provides a strong and resilient testing process that offers 100% accuracy, agility, and consistency. It is an outstanding technology when it comes to handling the complex, sensitive, and diverse system of the banks’ core software. RTA help in automating the testing of various test cases individually and perform regression testing each time a change is made in the system.

Conclusion

As the software testing trends is empowering automation so that manual testing is fully supported with the powerful test automation practices. There must be a clear distinction where we can leverage the power of automation testing to its full potential and where manual testing will be required to enhance the product and service quality. Businesses can leverage open-source and low code technologies to further their claim in the market.

Continuous development is the driving force of business success and as the demand for quicker, qualified and more accurate software emerges, test automation tools become a key to continuous development. Thus, the chief trend in the upcoming years is increased test adoption at the early stage of software development to ensure the quality of any product. without a proper method of testing their ideas will not keep up with mass digital adoption.

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