The evolving role of humans and software testing in the age of automation

Artificial intelligence is transforming software testing and quality assurance, though organizations face both technical and cultural challenges in adoption.

Current state of AI in software testing: Generative AI is becoming a crucial component of software quality engineering, with 68% of organizations now using it for testing purposes.

• 29% of organizations have fully integrated Gen AI into test automation, while 42% are exploring its potential
• Cloud-native technologies and robotic process automation are prevalent in modern test automation approaches
• The average level of test automation has reached 44%, driven by newer and smarter automation tools

Key challenges and concerns: Organizations face several obstacles in implementing AI-driven testing solutions.

• 61% of organizations express concerns about data breaches related to generative AI
• 57% cite lack of comprehensive test automation strategies as a barrier
• 64% point to legacy system dependencies as an impediment to automation
• 56% report that quality engineering is not viewed as a strategic activity in their organizations

Organizational dynamics: The relationship between quality engineering and Agile development teams is evolving.

• Only one-third of quality engineers currently participate in Agile teams
• The number of standalone quality engineers is expected to increase from 27% to 38%
• Cross-skilling initiatives have reduced skills-related bottlenecks from 37% to 16% year-over-year

Implementation strategies: Success in AI-driven testing requires a methodical approach.

• Organizations need to develop enterprise-wide automation strategies with clear objectives
• Experimentation with multiple AI approaches is recommended before committing to specific solutions
• Quality engineering automation tools should be streamlined and compatible with emerging technologies
• Business KPIs should be tied to quality engineering initiatives

Workforce implications: The role of quality engineers is transforming rather than disappearing.

• Gen AI is expected to enhance rather than replace quality engineers
• Organizations are increasingly seeking full-stack quality and software development engineers
• Quality engineering teams require time to realize the full benefits of AI integration

Industry trajectory: AI’s role in software testing continues to expand in scope and complexity.

• The evolution of large language models and AI tools like Copilot is enabling seamless integration into existing development lifecycles
• Testing requirements now extend to AI-generated code and end-to-end software chains
• Quality engineering is transitioning from testing human-written software to validating AI-generated code

Looking ahead: The integration of AI in software testing represents a fundamental shift in quality assurance, though success will require organizations to balance technological capabilities with cultural transformation and strategic alignment with business objectives.