By Mr Pradeep Govindasamy
AI is ushering in a new era for Quality Engineering (QE), transforming how software quality is maintained. As organizations face growing demands to deliver complex systems faster, AI is driving a significant shift in the QE landscape. By automating repetitive tasks, generating intelligent test cases, and utilizing predictive analytics, AI accelerates testing while improving accuracy and early defect detection.
According to a recent Gartner report, by 2028, GenAI-based tools will write 70% of software tests, with 80% of enterprises incorporating AI-augmented testing into their engineering workflows -- up from just 15% in 2023. This shift raises important questions for both software testers and business owners.
For CIOs and CTOs, AI is becoming indispensable in delivering superior software faster. While manual testing still has a place, it now operates within an AI-enhanced framework that merges automation with human insight to produce more reliable results. Previously, programming skills were essential for software testers, but with the rise of AI/ML and no-code testing tools, AI expertise is now more valuable than coding skills.
However, this evolution won't happen overnight. The same report predicts that by 2028, most companies will use AI tools to code software. While many haven't fully adopted AI for testing yet, the growing volume of code -- partly driven by AI -- will make no-code testing crucial. IT leaders will need to make informed decisions on which AI-augmented tools to invest in to keep pace with this shift.
The Role of AI in Modern Quality Engineering
Test Automation: One of the most significant applications of AI in QE is test automation. AI-powered test automation tools can analyze application behavior, identify patterns, and automatically generate and execute test cases. This not only reduces the time and effort required for manual testing but also ensures consistency and repeatability across test cycles. Intelligent Test Case Generation: AI algorithms can analyze application requirements, code, and user behavior to automatically generate test cases that cover a wide range of scenarios. This intelligent test case generation helps to ensure comprehensive coverage and reduces the risk of missed defects. AI in Defect Detection and Prediction: AI can be used to analyze application logs, user behavior, and other data sources to detect defects in real-time. By identifying patterns and anomalies, AI-powered defect detection tools can catch bugs early in the development process, reducing the cost and effort required for remediation.AI can also be used to predict the likelihood of defects based on factors such as code complexity, developer experience, and past defect history. This predictive analytics can help organizations prioritize testing efforts and allocate resources more effectively. Test Coverage Optimization: AI can help organizations optimize their testing efforts by identifying areas of the application that are most critical or high-risk. By analyzing factors such as user behavior, code complexity, and past defect history, AI can help organizations focus their testing efforts on the areas that matter most. Generating Synthetic Data: One of the challenges of testing modern software systems is the need for large volumes of realistic test data. AI can be used to generate synthetic data that mimics real-world user behavior and data patterns. This synthetic data can be used to test the application under a wide range of scenarios without the need for access to sensitive production data. Auto Healing: AI can also be used to automatically "heal" test scripts when the application under test changes. By analyzing the application's behavior and identifying patterns, AI-powered auto healing tools can automatically update test scripts to account for changes in the application, reducing the need for manual maintenance and ensuring that tests remain up-to-date. Security Testing: AI/ML-based tools are employed to detect and prevent injection-based attacks such as SQL injection, XSS, HTML injection, and command injection by scrutinizing user input. This proactive approach aims to safeguard against data loss, corruption, security breaches, data theft, and denial of service attacks.
Benefits of AI in Quality Engineering
The integration of AI into Quality Engineering offers a range of benefits, including:
Increased Efficiency: AI-powered QE tools can automate repetitive tasks and optimize testing efforts, allowing organizations to deliver high-quality software faster and more efficiently. Saving Time & Money: By reducing the need for manual testing and catching defects early in the development process, AI-driven QE can save organizations significant time and money. Enhanced Accuracy and Coverage: AI can help organizations achieve more comprehensive test coverage and identify defects that might be missed by manual testing. Risk Mitigation: By predicting defects and identifying high-risk areas of the application, AI can help organizations mitigate the risk of costly failures and downtime. Autonomous Testing: AI can be used to create self-healing, self-optimizing testing frameworks that can adapt to changes in the application and environment without the need for manual intervention. Performance Testing: Utilizing deep learning, predictive performance modeling enhances accuracy by analyzing complex data relationships. Integrated with real-time monitoring tools, these models provide continuous feedback and automatic adjustments. AI-driven systems leverage predictive insights to autonomously manage and mitigate performance issues.
The Future Outlook
As AI continues to advance and mature, its impact on Quality Engineering is only likely to grow. In the coming years, we can expect to see even more sophisticated AI-powered QE tools that can adapt to changes in real-time, learn from past experiences, and collaborate with human testers to deliver the highest levels of quality and reliability. Some of the key AI technologies that are likely to shape the future of QE include:
Reinforcement Learning: AI systems that can learn and adapt based on feedback and rewards, allowing them to optimize testing strategies over time. Natural Language Processing: AI that can understand and interpret natural language, allowing for more intuitive and user-friendly testing frameworks. Explainable AI: AI systems that can provide clear explanations for their decisions and recommendations, making it easier for human testers to understand and trust the results.
As the pace of technological change continues to accelerate, the need for effective Quality Engineering has never been greater. By embracing AI and other cutting-edge technologies, organizations can stay ahead of the curve and deliver the high-quality software that their customers demand.
(The author is Mr Pradeep Govindasamy, President and CEO, QualiZeal, and the views expressed in this article are his own)
