AI-Augmented Software Engineering (AIASE)

• 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

AI-Augmented Software Engineering (AIASE) is the application of artificial intelligence to enhance and automate tasks within the software engineering process.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software professionals who wish to leverage AI and machine learning to improve efficiency and innovation in software development.

By the end of this training, participants will be able to:

• Understand the role of AI and machine learning in automating software development tasks.
• Implement AI tools to generate code, tests, and documentation.
• Apply AI techniques for code optimization, quality assurance, and debugging.
• Integrate AI into the DevOps and CI/CD pipelines for improved deployment strategies.
• Address ethical considerations and challenges in AI-augmented software engineering.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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