Generative artificial intelligence or GenAI is reshaping quality engineering (QE), making it more efficient, intelligent, and scalable. While current GenAI applications show promise in boosting efficiency and coverage, the future promises greater potential. According to a recent report in CXOtoday, GenAI will produce 70% of software tests by 2028, while 80% of enterprises will have embedded AI-driven testing into their engineering workflows. This blog explores how GenAI is transforming QE today and how its projected evolution can amplify capabilities and establish robust AI assurance for reliable, AI-driven systems.

Today’s Reality: GenAI for Revolutionizing QE

GenAI is already redefining how QE teams operate and accelerate key processes across the software development lifecycle. Some of its applications include:

• Intelligent test design: Analysis of requirements to identify potential gaps and generate a wide range of test cases beyond traditional approaches
• Smart test data generation and management: Accelerated test cycles by generating realistic synthetic data, anonymizing sensitive information, and efficiently identifying as well as managing data dependencies
• Efficient test automation and optimal execution: Simplified and fast test automation through intelligent selection and execution of tests, analysis of results to identify failures and root causes, self-healing of scripts, and orchestrated test execution sequences based on historical data
• Predictive analytics for actionable insights: Real-time visibility into testing progress and key metrics and flagging of defect hotspots through analysis of production incidents and logs to enable continuous test suite augmentation and data-driven decision making

Need of the Hour: Standardize and Democratize GenAI-led QE Practices and Platforms

Integrating GenAI solutions with existing infrastructure and test artifacts is as important as reskilling the workforce to operate GenAI applications. Hence, IT organizations are building scalable QE platforms that enable seamless access to AI models, allowing easier training, fine-tuning, deployment, and monitoring to generate impactful outcomes. The Infosys Quality Engineering AI Platform, a modular, layered, and model-agnostic solution, is at the forefront of our offerings and enables scalability and democratization of GenAI for QE.

Tomorrow’s Possibility: Enhancing Capabilities and Building Trust with AI Assurance

In future, GenAI in QE will focus on amplifying current capabilities while reinforcing AI assurance. It will enable:

• Hyper-automation: Leveraging sophisticated AI agents to manage entire QE workflows, right from test planning to execution and reporting
• Contextualized testing: Going beyond basic test case creation to generate contextual and domain-intensive tests that dynamically adapt to user journeys and application states
•  AI-driven optimization: Optimizing test suites in real time through prioritization of high-impact tests while adjusting execution based on risk, criticality, and application conditions
• Explainable AI for QE: Integrating deeply into QE processes with the ability to explain how AI makes decisions to build trust and enable human oversight
• Robust AI assurance: Emerging frameworks to address bias and model drift, while providing explainability, robustness, and security for GenAI-powered applications
• Continuous AI assurance: Evolving AI assurance into an ongoing process, with continuous monitoring and feedback loops to maintain quality and reliability
• Human-AI collaboration: Defining human-AI partnerships to strengthen the future of QE, where QE professionals act as AI custodians, guiding AI processes and ensuring ethical use

Strategic Imperative: Building Trust in AI-powered Enterprises

As organizations embrace GenAI to accelerate QE and enhance quality, the strategic need is to prioritize AI assurance. The trustworthiness of AI-driven applications is pivotal for business success. The future of QE lies in balancing speed with trust, creating a powerful synergy that drives sustainable business value and helps build AI-powered enterprises.

The Infosys AI Assurance Platform offers comprehensive capabilities for integrated model assurance (including non-functional aspects) and coverage, along with Responsible AI guardrails that ensure accurate, stable, ethical, and trustworthy AI systems.

Conclusion

The rapid evolution of GenAI is an unprecedented opportunity for QE transformation. To effectively harness this potential amidst current and emerging complexities, organizations must assess their current state to envision and evolve into an AI-first QE landscape. This involves evaluating skills, prioritizing use cases, and revisiting processes in order to create a clear roadmap for structured AI adoption.