Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AIASE
- Overview of AI in software engineering
- History and evolution of AIASE
- Key concepts and terminology
AI Technologies in Software Development
- Machine learning basics
- Natural language processing (NLP) for code
- Neural networks and deep learning models
Automating Software Development with AI
- AI tools for generating boilerplate code
- Automated code refactoring and optimization
- Functional and unit test code generation
- AI-assisted test case design and optimization
Enhancing Code Quality with AI
- AI for bug detection and code reviews
- Predictive analytics for software maintenance
- AI-powered static and dynamic analysis tools
- Automated debugging techniques
- AI-driven fault localization and repair
AI in DevOps and Continuous Integration/Continuous Deployment (CI/CD)
- AI for build optimization and deployment
- AI in monitoring and log analysis
- Predictive models for CI/CD pipelines
- AI-based test automation in CI/CD workflows
- AI for real-time error detection and resolution
AI for Documentation and Knowledge Management
- Automated generation of docstrings and documentation
- Knowledge extraction from codebases
- AI for code search and reuse
Ethical Considerations and Challenges
- Bias and fairness in AI tools
- Intellectual property and licensing issues
- Future of AI in software engineering
Hands-On Projects and Case Studies
- Working with popular AI tools in software engineering
- Case studies of AIASE in industry
- Capstone project: Developing an AI-augmented software application
Summary and Next Steps
Requirements
- An understanding of software development processes and methodologies
- Experience with programming in Python
- Basic knowledge of machine learning concepts
Audience
- Software developers
- Software engineers
- Technical leads and managers
14 Hours