According to Gartner, the increasing demand for faster software deployment poses challenges in scaling the testing process. Currently, quality engineering teams struggle to keep pace with the accelerated rate of change due to dynamic business needs and increasing complexity of applications. This leads to problems in mapping the testing approach to requirements and tracing the user journey through reliable test suites. Continuous delivery of new features and functionalities adds to these complications. There is a need for smarter and more efficient test automation solutions offering improved productivity, lower investment, and optimum test coverage.

Riding the AI-led Automation Wave

Existing test automation tools are fragmented and a majority of them are not technology-agnostic. In addition, technology upgrades lead to multiple iterations of changes in test resources such as automation scripts, object modifications, as well as dynamic changes to requirements at the UI, API, and backend layers. These effort-intensive activities lead to delays in delivery timelines, reduced productivity, and regressive automation percentages. These challenges in the current landscape reinforce the need for a technology-agnostic automation framework. With the growing need for accurate and precise regression test-suites, AI-led test automation is a business-critical asset.

The AI Edge to Quality

Artificial intelligence (AI) and machine learning (ML) help overcome the gaps in traditional testing practices. AI-augmented tools identify potential risks associated with proposed changes and automate business decisions based on historical data. AI-powered algorithms can autonomously explore complex applications to generate meaningful test cases as well as create test cases based on real-time usage patterns of end users.

Together, AI and ML redefine the quality engineering paradigm by eliminating duplicate tests to avoid redundancy, providing high quality of test data through AI-driven test data management, and enhancing predictability with feedback-based supervised ML models.

Sample use cases for AI-based test automation

Here are a few use cases implemented by leveraging AI techniques such as natural language processing (NLP) and supervised machine learning (SML) to augment automation practices.

• Generate autonomous test scripts at a faster pace compared to conventional test automation
• Adapt to changes in code without changing the test scripts using self-healing capabilities
• Provide greater test coverage for application testing
• Identify impacted regression test scripts based on code changes for a specific release
• Leverage machine learning techniques to perform root cause analysis using logs and suggest rapid fixes

By embedding AI-based game-changing tools and techniques in quality engineering, enterprises can achieve a 5-10 times improvement in productivity. Businesses can significantly lower maintenance effort by up to 80% by leveraging AI-powered self-healing capabilities. AI-led quality engineering (QE) helps derive business value along multiple dimensions such as improved accuracy, faster time to market, and an enhanced competitive edge.

Jumpstart with AI and Automation

Enterprise-wide growth and transformation initiatives require better integration of business with IT. Only enterprises hyper-focused on quality will be able to augment their value proposition and achieve better business outcomes. In a world where technology disruptions are frequent, enterprises must have a well-defined roadmap and processes to rapidly adopt niche technologies.

AI-augmented quality engineering transformation requires experts with cutting-edge software engineering skills, domain expertise, and the ability to orchestrate quality activities across agile development initiatives. A well-defined AI-led automation strategy can help enterprises achieve scalability, security, agility, and a high degree of innovation.