How Generative AI is changing software testing

Generative AI solutions (GenAI) have moved from being an experimental technology to a practical enabler within software testing and quality engineering. It’s not just a buzzword or a tech industry trend, it’s a capability that, when used intelligently, can transform how teams deliver quality outcomes.

In the fast-paced world of software delivery, the demands on quality assurance (QA) teams have never been greater. There’s constant pressure to test faster, cover more scenarios, and ensure quality before release. GenAI offers the potential to meet these demands by producing test artefacts, data, and even ideas at unprecedented speed. But technology alone isn’t the answer.

At Ten10, we believe in a pragmatic, people-first approach: using AI to enhance, not replace, human capabilities. The real power of GenAI emerges when it works alongside skilled testers, supporting their decision-making, extending their reach, and allowing them to focus on high-value activities that require creativity, judgment, and context awareness.

Strengths of Generative AI in software testing

When applied in the right way, GenAI can significantly boost the effectiveness of quality engineering. Key strengths include:

• Rapid test case generation – GenAI can automatically produce expansive test suites directly from requirements or user stories. This reduces the time testers spend on initial script creation, freeing them to focus on validation, optimisation, and higher-risk scenarios.
• Realistic test data creation – By synthesising data that looks and behaves like real-world inputs, GenAI allows QA teams to test more thoroughly without breaching privacy rules. Diverse datasets can help identify defects that only occur under specific, less common conditions.
• Smarter script maintenance – Maintaining automated test scripts can be labour-intensive, especially when systems change frequently. GenAI-powered self-healing scripts can automatically adjust to UI or API modifications, while AI-assisted debugging can speed up issue resolution.
• Exploratory test prompts – Even experienced testers can overlook certain edge cases. GenAI can suggest alternative paths, unusual input combinations, or environmental conditions that may expose weaknesses in the application.

Risks, limitations and challenges of GenAI in QA

While the opportunities are compelling, it’s important to acknowledge the risks and limitations:

• Hallucinated outputs – AI can generate scripts or data that look correct but are fundamentally flawed, leading to wasted time or even missed defects.
• Lack of business context – GenAI may not fully grasp the nuances of industry regulations, customer expectations, or unique business processes; factors a human tester naturally considers.
• Privacy and compliance risks – If a GenAI tool is trained on unvetted datasets, it could inadvertently expose or misuse sensitive data. This has clear implications for GDPR compliance and broader security.
• Immature tooling – Many GenAI QA tools are still in early development. They may lack integration with enterprise-grade test management platforms, making adoption challenging.

Over-reliance on GenAI without appropriate safeguards can result in costly production defects or compliance breaches. This is why Ten10 combines AI-powered testing with our Security & Penetration Testing and DevOps & Agile Delivery services, ensuring the adoption of AI is controlled, secure, and aligned to business goals.

Why testers are still essential

The most effective AI testing workflows place humans firmly in the loop. GenAI can accelerate test creation, but it cannot replicate human curiosity, critical thinking, and adaptability.

For example:

• Exploratory testing often relies on intuition and past experience, something AI cannot replicate.
• Domain expertise enables testers to spot subtle risks that would otherwise go unnoticed.
• Contextual decision-making ensures that testing aligns with business priorities and customer needs.

At Ten10, we see AI as a co-pilot. Our consultants use it to extend their capabilities, not replace them. This approach ensures quality remains high while embracing innovation.

Ethics and Regulations for Testing with Responsibility

The ethical use of AI is non-negotiable. In testing, this means protecting sensitive data, adhering to GDPR, ensuring auditability of AI-generated outputs, and maintaining transparency in how models operate.
Ten10 takes a responsible AI approach by:

• Training Academy Consultants in AI ethics and compliance.
• Embedding audit trails for AI-driven decisions.
• Using only vetted, secure AI tools that meet our standards for reliability and privacy.

By prioritising ethics, we help clients adopt AI in a way that is both innovative and trustworthy.

Our approach to using Intelligent Automation to streamline operations

Ten10 uses AI to enhance automation, decision-making, and software delivery in practical, measurable ways. Our intelligent automation solutions apply machine learning to recognise patterns, classify data, and make predictions, while natural language processing (NLP) interprets and extracts information from unstructured text such as contracts, emails, or medical notes. In quality engineering, AI generates and prioritises test cases, predicts defects, and creates realistic test data to accelerate release cycles. And in cloud and DevOps, AI assists with coding through tools like GitHub Copilot, automates infrastructure provisioning, and uses predictive analytics to preempt downtime or service issues. By combining AI with RPA, Ten10 automates both repetitive tasks and more complex, cognitive processes, delivering faster, more accurate, and insight-rich outcomes for clients.

Ten10 applies its AI capabilities across multiple sectors, tailoring solutions to specific industry challenges:

• Public Sector – Streamlining services, ensuring compliance, improving security, and reducing operational costs.
• Healthcare – Automating admin tasks, enhancing data-driven decision-making, and improving patient care efficiency.
• Financial Services & Banking – Accelerating loan processing, enhancing fraud detection, and optimising compliance monitoring.
• Retail – Improving inventory management, streamlining supply chains, and delivering personalised customer experiences.
• Legal – Automating contract reviews, enabling intelligent document extraction, and simplifying compliance and case management.

Ten10’s perspective: A hybrid, practical approach to GenAI

GenAI alone is not the solution. But in the hands of skilled testers, it can be a powerful enabler of speed, coverage, and innovation.

At Ten10, we combine the best of both worlds: advanced AI tools to accelerate delivery, and human expertise to ensure accuracy, relevance, and compliance. This hybrid approach delivers measurable business outcomes; securely, responsibly, and with precision.

Explore our AI solutions and discover how we can help you adopt GenAI in a way that enhances your quality engineering strategy, not complicates it.