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AI for Software Testing: Introduction and Best Practices
Generative AI can support testing if its outputs are verified. Explore uses for preparing scenarios and analyses, then assess their relevance to your product. Develop practices that connect AI assistance, test quality and tester judgement.
- Duration
- 2 days 14 hours
- Code
- DEV015FR Code
Presentation
Artificial intelligence is transforming software testing with innovative ways to automate, accelerate and improve validation reliability. This course provides a practical introduction to AI in software testing through real use cases and hands-on work with effective tools.
Participants discover how AI can optimise test case generation, improve script automation and analyse results effectively. Particular attention is given to prompt engineering, essential for using generative AI in a quality context.
Beyond technical aspects, the course addresses ethical issues and AI’s limitations in testing, helping participants adopt an innovative and responsible approach.
Objectives
By the end of this software testing and application acceptance course, you will be able to:
- understand AI fundamentals and its main software testing applications;
- master prompt engineering techniques to generate test cases effectively with AI tools;
- use AI to improve test automation, including script generation and result analysis;
- assess risks, limitations and ethical issues associated with AI in testing processes.
Program
Module 1: introductions
- Individual introductions and sharing course expectations.
- Overview of the program and objectives.
- Clarifying challenges and aligning on shared objectives.
Module 2: introduction to AI in software testing
- AI definitions and fundamentals: machine learning, deep learning and NLP.
- Overview of AI applications in software quality assurance.
- Real use cases and market trends.
Case study:
- Analyse projects that have integrated AI into test management.
Module 3: prompt engineering and test case generation
- Prompt engineering principles: designing optimal instructions for generative AI.
- AI tools and platforms: ChatGPT, Copilot and Testim.io.
- AI-assisted test case creation.
Practical work:
- Write and test prompts to generate functional test cases automatically.
Module 4: test automation with AI
- Intelligent automation: from script generation to automated execution.
- Using AI to analyse test results.
- Integrating AI into automation tools, such as Selenium with AI and Test.AI.
Practical work:
- Automate a series of tests using AI-generated data.
Module 5: challenges, limitations and ethics of AI in testing
- Reliability, bias and interpretation of AI results.
- Traceability and transparency challenges.
- Legal and ethical considerations in software quality.
Group discussion:
- Acceptable limits of AI use in quality projects.
Audience
This course is intended for:
- Testers.
- Quality managers.
- Project managers.
- Anyone seeking to integrate AI to optimise software testing.
Prerequisites
This course requires:
- previous software testing or quality assurance experience;
- test automation fundamentals.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
- Case study
Course highlights
- A clear, accessible introduction to AI in testing.
- Immediate practical application through concrete exercises.
- Innovative tools for test automation.
- Ethical considerations for responsible AI integration.
- An expert instructor in both software quality and artificial intelligence.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.