By 2027, up to 90% of testing processes are expected to be automated¹. The advent of next-generation automation in quality assurance (QA) and quality engineering (QE) brought in by AI and genAI rapidly redefines the traditional boundaries of software development, testing, and operations. This shift raises a crucial question: What role will test and quality engineers play in a future where large portions of the SDLC are increasingly getting autonomous?

The shift to autonomous testing

AI-driven automation is already making strides in test case generation, regression testing, error detection, and defect prediction. GenAI enhances this by using extensive datasets to predict potential vulnerabilities and automatically generate tailored test scripts. Over time, these capabilities will enable autonomous self-healing systems to detect, report, and fix issues in real-time.

However, while smart automation will handle many repetitive, manual testing tasks, it doesn't entirely negate the need for human intervention. Instead, it shifts the role of test and quality engineers towards higher-value tasks.

The new role of test engineers:

AI assurance engineers: As AI becomes integral to testing, engineers will need to ensure that the AI models are accurate, ethical, and free from bias. This includes validating AI decisions, setting up testing standards, and creating governance frameworks for AI tools.
Model trainers and curators: Since AI requires data to function, engineers will train and curate data for AI models, ensuring the use of high-quality datasets and maintaining feedback loops for continuous improvement.
Security auditors: Skilled quality engineers will need to ensure that genAI-enabled systems do not have lax data security measures, which can publicly expose proprietary information, trade secrets, or customer information. A thorough review of genAI outputs is required to avoid compliance violations, contract breaches, and copyright infringements.
Quality architects: Engineers will evolve into “quality architects,” whose primary responsibility will be to design the testing frameworks and environments in which AI-powered systems can operate. They will also identify scenarios where using genAI may not be appropriate.
UX engineers: Moving beyond functional testing, quality engineers can shift towards user experience (UX) engineering, focusing on the overall impact on customers. They can use machine learning to analyze feedback from social media, app stores, and production logs.
Exploratory and risk-based testing: While AI can automate structured tests like regression testing, engineers will be critical for exploratory testing that requires creativity and contextual understanding. They will also handle risk-based testing to identify scenarios AI might miss or misinterpret.

Continued relevance of quality engineering

Although AI-driven automation will reduce the need for traditional hands-on testing, the expertise of quality engineers will remain essential in several areas:

Test strategy and planning: Test strategy involves balancing various factors such as risk, cost, time constraints, and project scope, aligning with business goals and stakeholder priorities. While AI can assist with data analysis and past project insights, creating a tailored test strategy requires human experience and judgment, especially in dynamic or novel contexts.
Expanded test coverage: Future quality engineers will use hybrid methods, combining AI’s speed and efficiency in analyzing user stories and acceptance criteria to generate test cases for a wider range of scenarios and user personas, reducing the risk of defects reaching production.
Continuous quality assurance: Maintaining continuous quality across the SDLC will require leveraging AI-augmented testing solutions within Agile frameworks. These solutions include automated test case generation, defect prediction, triaging, test optimization, and result summarization.
Reliability and resilience engineering: The engineering focus will be on building resilience into autonomous systems to handle unexpected events and edge cases while refining AI-generated test suites to prevent outdated coding practices.
Security and compliance: Emphasis on data security, vulnerability assessments, and regulatory compliance will only grow, especially in sensitive industries like healthcare and finance.
Cost and performance: As genAI matures, engineers will need to balance system costs and performance, ensuring that public LLMs are not overused for trivial queries, which could drive up expenses.
Script migration and updates: Traditional script migration can be slow and laborious. Quality engineers will build and reuse prompt libraries to automate genAI-driven script migrations from legacy to modern frameworks and use ML models to update scripts as application components (UIs, workflows, etc) change.
Complex systems integration: In a connected digital ecosystem, engineers will focus on integrating AI-driven applications with CI/CD pipelines, security solutions, automated testing, and deployment tools to optimize for speed and quality.

At Virtusa, we have a team of seasoned experts with next-gen advanced automation capabilities and AI-driven solutions, including our genAI platform - Helio Assure. It enables us to deliver unparalleled support and drive future-ready outcomes for our clients. Recently, our team leveraged GitHub Copilot for a telecom customer to increase test automation productivity by 40% with enhanced coverage. In another instance, we improved the efficacy of a genAI assistant for project managers, that provided reliable answers and smart recommendations with an accuracy rate of over 99%.

The future of quality assurance lies in a seamless partnership between human expertise and AI-driven automation. As SDLC processes become increasingly autonomous, test and quality engineers will shift to more strategic roles focused on design, oversight, and integration. Their expertise will ensure AI operates safely, ethically, and reliably while driving efficiency and enhancing effectiveness in managing modern business applications.

^{1. The Future of Test Automation – Trends and Predictions for 2025 and Beyond. QualiZeal. Published December 4, 2024.}^{The Future of Test Automation – Trends and Predictions for 2025 and Beyond - QualiZeal}^.

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