Transform your software development lifecycle (SDLC) with the power of generative AI. Learn to enhance efficiency, creativity, and quality by integrating AI into every phase of the SDLC, from design to deployment.
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Generative AI in Software Engineering Learning Path
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Course 1
GenAI in Requirement Gathering Phase
Course 2
GenAI in Design Phase
Course 3
GenAI in Build Phase
Course 4
GenAI in Testing Phase
Course 5
GenAI in Deployment Phase
Learn essential skills
Generative AI in Software Engineering Learning Path
Harness the power of generative AI in software engineering
This learning path explores how generative AI transforms the software development lifecycle. With topics ranging from requirement gathering to deployment, you’ll learn how to apply AI for efficiency, innovation, and ethical development practices.
Learning objectives
- Understand generative AI fundamentals and its role in the SDLC
- Enhance requirement gathering and project planning with AI tools
- Use AI for code generation, optimisation, and testing
- Automate deployment processes and maintain documentation with AI
- Apply ethical AI practices to software engineering
What you'll learn
Generative AI revolutionises software engineering, offering tools and techniques to streamline the entire software development lifecycle. This learning path empowers you with practical skills to integrate AI into every phase of SDLC, enhancing productivity, creativity, and quality while addressing real-world challenges and fostering ethical AI practices.
Generative AI fundamentals
Discover the core principles and capabilities of generative AI and its transformative role in modern software engineering. This module covers essential AI models and technologies, including OpenAI GPT and DALL-E, providing a foundation to understand how AI-driven solutions can automate processes, generate creative outputs, and address complex challenges throughout the SDLC.
AI in requirement gathering
Learn how generative AI enhances the requirements phase by automating documentation and analysis. Use tools like OpenAI GPT to identify user needs, prioritise features, and ensure alignment with business objectives. This module equips you with techniques to streamline requirement gathering, reduce manual effort, and improve collaboration among stakeholders.
AI in testing and optimisation
Explore how AI-powered tools like AWS CodeGuru optimise code quality, identify bugs, and automate testing processes. This section focuses on improving reliability, reducing time-to-market, and ensuring robust software performance. You’ll gain hands-on experience with cutting-edge AI testing solutions, making your development process more efficient and precise.
AI in deployment and maintenance
Generative AI simplifies deployment pipelines, enabling seamless rollouts and faster updates through automated CI/CD processes. Additionally, AI-driven NLP tools streamline documentation generation and upkeep, ensuring accurate and up-to-date materials. This module highlights how these advancements enhance deployment efficiency and maintain operational excellence over the software’s lifecycle.
Ethical use of AI
Understand the ethical considerations of implementing AI in software engineering. This module addresses potential biases in AI systems, the risks associated with their use, and strategies to mitigate such challenges. By exploring real-world examples, like facial recognition software biases, you’ll learn to align your AI practices with ethical and inclusive standards.
What's included
- Generative AI in Software Engineering Learning Path training
- 15+ practical case studies
FAQs
Discover how this learning path equips you to integrate generative AI into software engineering.
Do I need prior AI experience to take this course?
No prior AI experience is necessary. However, a background in software engineering or related fields, will help you get the most out of your learning.
What tools will I learn to use in this course?
You’ll gain hands-on experience with OpenAI GPT, DALL-E, AWS Code Whisperer, CodeGuru, and other cutting-edge tools for software engineering.
What kind of hands-on projects will I complete in this learning path?
You’ll work on projects like optimising Agile requirement prioritisation using OpenAI Codex, designing a UI for a digital marketing company, and identifying performance bottlenecks with AWS CodeGuru. These projects are designed to provide practical experience with generative AI tools in real-world scenarios.
How does this course address ethical concerns with AI?
The course includes a dedicated module on ethical AI practices, covering topics like bias mitigation, responsible use of generative AI, and real-world case studies. You’ll learn strategies to ensure your AI implementations are fair, transparent, and aligned with industry standards.
What our customers say
"Good, experienced teacher, well balanced and clear course."
Jacky Droushoudt, ILX Learner
"I had an excellent experience with the service provided by ILX. From the initial registration to the completion of the course, every aspect was handled professionally and efficiently.”
Andrew Addy Asante, ILX Learner
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