How generative AI for software testing is transforming QA: ALTEN’s vision and expertise

Generative AI is profoundly transforming the software testing industry. Far from replacing human expertise, it enhances testers’ capabilities through intelligent prompting and next-generation automation. This technology now enables testing of complex applications in real-world conditions while automatically generating scenarios covering a wide range of test cases. Companies can deploy large-scale testing strategies, simultaneously validating multiple applications with unprecedented efficiency, boosting overall testing efficiency across projects.

ALTEN, a leader in Engineering and IT Services and a Global Partner of ISTQB®, stands at the forefront of this transformation, actively contributing to the development of international standards.

Paradigm shift in software testing with generative AI 

As Alessandro Collino, software testing expert for ALTEN in Italy and co-author of the ISTQB® CT-GenAI syllabus, highlights: “Generative AI does not replace testing expertise; it amplifies it through intelligent prompting.” This marks a major turning point in testing methodologies.

Traditionally, AI adoption in software testing relied on experimental and reactive approaches. Teams tested different tools without a holistic vision or structured methodology. Today, with standardised frameworks like the ISTQB® CT-GenAI syllabus, the software testing industry has, for the first time, a systematic approach to transform software testing from reactive experimentation into strategic AI integration.

This evolution relies on advanced machine learning technologies enabling AI models to understand code patterns, identify anomalies, and generate relevant test case scenarios. Machine learning forms the technical foundation of generative AI, allowing systems to learn from vast repositories of historical test data.

Critical challenges in AI-driven test automation 

Hallucinations and reasoning errors

Si les opportunités sont transformatrices, les défis sont tout aussi significatifs. Alessandro Collino insiste sur un point crucial : « La gestion des hallucinations et des erreurs de raisonnement nécessite une vigilance constante et des frameworks de validation ».

Generative AI models may produce results that appear plausible but are factually incorrect. In AI-driven automated testing, these hallucinations can lead to invalid test cases failing silently, false positives delaying deployments, false negatives letting critical bugs slip through, overconfidence in unvalidated results, and generation of syntactically correct but logically incorrect test case code. 

Bias challenges in generative AI

Beyond technical risks, integrating generative AI in software testing raises significant ethical questions. A central challenge is ensuring AI systems do not reproduce or amplify biases present in training data. Piotr Wierski, Head of Quality Assurance, Test & Embedded Practice for ALTEN in Poland, asks: “How can we prevent errors related to data bias, protect user privacy, and define accountability for AI-driven decisions?”

Enhancing test tools automation through a standardised framework in QA 

This is precisely why the ISTQB® CT-GenAI syllabus is fundamental. The Certified Tester Specialist Level – Testing with Generative AI (CT-GenAI) syllabus provides a unified knowledge base across five critical areas:

Foundations of Generative AI: understanding LLMs and their architecture
Mastering Prompt Engineering: techniques for effective AI model interaction
Risk Management for AI: identifying and mitigating biases and hallucinations
Implementing LLM-powered infrastructures: architectures and automation tools for QA integration
Creating Organizational Transformation Strategies: deploying AI strategically at the enterprise level

Alessandro Collino notes: “This certification establishes a unified foundation covering critical areas from prompt engineering to organizational transformation.”

ALTEN’s vision: leveraging generative AI for software testing excellence 

Contribution to the ISTQB® CT-GenAI syllabus

ALTEN played a major role in developing this new international reference. Of the five global authors of the syllabus, two are ALTEN experts, including Alessandro Collino, who contributed to the full framework. This involvement reflects ALTEN’s ongoing commitment to:

Staying at the forefront of AI testing advancements
Supporting best practices in software quality
Contributing to international industry standards
Sharing field expertise gained from thousands of client projects

ALTEN’s participation ensures recommendations are both theoretically robust and pragmatic for real-world test case applications.

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Global deployment and training

As a Global Partner and Accredited Training Provider of ISTQB®, ALTEN actively deploys the CT-GenAI syllabus internationally. Instructor teams are already active in:

France
Germany
Italy
The Netherlands
Poland
Spain

The CT-GenAI syllabus also serves as the foundation for training test experts within ALTEN’s Quality Engineering Centres of Excellence (COEs), facilitating rapid and structured adoption of AI-driven test automation practices. 

ALTEN in Poland: ISTQB® Platinum Partner and AI testing expertise 

A meaningful Platinum ISTQB® Partnership

ALTEN in Poland has achieved the prestigious ISTQB® Platinum Partnership status, reflecting long-term excellence and commitment in software testing. Piotr Wierski explains: “Achieving Platinum status is a major step in becoming a trusted solution provider and joining an elite group alongside our colleagues in Italy, France, and Spain.”

This status validates the tangible impact of ALTEN teams’ work. Over the years, they have developed recognised expertise facilitating rapid integration of emerging technologies like generative AI into test workflows.

Training and skills development excellence

ALTEN in Poland heavily invests in practical training, viewing skill development as a core pillar of quality. ISTQB® certification opens access to complex projects, including safety-critical products. The Polish team emphasises:

Hands-on workshops and continuous training in test automation AI tools
Knowledge sharing among testers, including AI-generated code and test cases
Regular client discussions promoting co-innovation
Mentorship programs for junior testers adopting generative AI

Organisations emphasising knowledge sharing and practical experience are highly valued by certified testers, creating a virtuous cycle of excellence.

Practical approach: maximising generative AI adoption 

A practice-oriented syllabus

CT-GenAI prioritises hands-on learning: over half of the training is practical. This enables professionals to quickly develop skills to use generative AI responsibly and effectively. Alessandro Collino emphasises: “For the first time, we have a systematic approach to transform software testing from reactive experimentation to strategic AI integration.”

Participants learn to:

Write effective prompts to generate high-quality test case code, leverage generative AI testing tools to enhance automation and accuracy
Validate and correct AI outputs
Identify limitations and risks of generative models
Integrate AI into existing CI/CD pipelines
Measure ROI of AI-driven test automation

Integration into existing workflows

The goal is to enhance, not disrupt, existing testing processes. Generative AI testing complements traditional approaches, allowing teams to design and optimise test cases alongside human expertise, creating a hybrid ecosystem where human and artificial intelligence collaborate effectively.

Trained professionals can:

Identify suitable use cases for generative AI
Assess potential risks and biases of AI models
Implement safeguards and robust validation mechanisms
Integrate AI-driven test automation seamlessly into CI/CD pipelines to accelerate deployment and feedback loops
Measure AI’s impact on deliverable quality and team productivity
Train and support colleagues in adopting new practices

Concrete use cases of generative AI in testing 

API Test Generation: Using OpenAPI/Swagger specifications, AI automatically generates tests covering all endpoints, including error and security scenarios.
End-to-End Test Creation: By analysing screenshots or Figma mock-ups, AI generates end-to-end test cases and corresponding automation code.
Log Analysis and Debugging: AI parses thousands of test execution logs to identify error patterns, suggest causes, and propose code fixes.
Realistic Test Data Generation: Automatically creates test datasets meeting complex constraints (GDPR, referential integrity, statistical representativeness).

Future outlook: towards an AI-Driven testing ecosystem 

Expected technological evolution

Greater democratisation: Generative AI testing tools become more accessible and intuitive for non-AI experts, with natural language conversational interfaces.
Specialised models: LLMs trained specifically for software testing, understanding technical architectures, bug patterns, and automation best practices.
Native DevOps integration: AI test automation embedded in CI/CD pipelines for faster defect detection and resolution.
Continuous self-improvement: AI learns from generated tests, improving and adapting strategies project by project.

Organisational transformation

Enhanced governance: Development of ethical frameworks and regulations for AI in critical testing processes.
New roles: Emergence of “AI Test Engineer” or “Test Automation Architect AI,” combining testing expertise and AI proficiency.
Optimised human-AI collaboration: Tasks intelligently allocated between humans and AI assistants based on strengths.

Conclusion: generative AI as an expertise amplifier, not a substitute

AI does not replace testing expertise; it amplifies it” (Alessandro Collino). The generative AI revolution in software testing does not eliminate the human testers.

ALTEN, through its contribution to ISTQB® CT-GenAI and global deployment via entities like ALTEN Poland, demonstrates that a structured, ethical, and practical approach to generative AI for software testing is both possible and necessary.

Organisations investing today in training teams on AI-driven automated testing and adopting standardised frameworks like CT-GenAI are strategically positioned for the future. They transform technological challenges into advantages, accelerating innovation cycles while maintaining high-quality standards.

Test automation reaches a new maturity level through generative AI. Testers mastering these technologies, understanding machine learning mechanisms, and generating effective test case code via intelligent prompts will be the key players of tomorrow’s software quality landscape.

The future of software testing will be hybrid, combining human creativity and judgment with generative AI’s power and efficiency. This future starts today, with pioneers like ALTEN leading the way and sharing expertise through initiatives like the ISTQB® CT-GenAI syllabus.