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Duration 14 hours
Course Outline
Introduction to AI-Augmented Software Engineering
- Summary of AI applications in software engineering
- Historical context and development of AIASE
- Fundamental definitions and nomenclature
AI Technologies Integrated into Software Development
- Foundations of machine learning
- Application of natural language processing to code
- Neural network architectures and deep learning models
Automation of Software Development Processes via AI
- AI utilities for generating standard code structures
- Automated code restructuring and performance optimization
- Generation of functional and unit testing code
- AI-facilitated test scenario design and refinement
Improvement of Code Quality Through AI
- AI applications in defect identification and code review processes
- Forecasting models for software maintenance activities
- AI-enabled static and dynamic analysis instruments
- Systematic debugging methodologies
- AI-based fault identification and remediation
AI Integration in DevOps and CI/CD Environments
- AI strategies for build process optimization and deployment
- AI utilization in system monitoring and log evaluation
- AI-driven test automation within CI/CD workflows
- Real-time error identification and resolution using AI
AI Applications in Documentation and Knowledge Retention
- Automated creation of code annotations and technical documentation
- Extraction of technical knowledge from code repositories
- AI-enhanced code retrieval and reuse strategies
Ethical Implications and Operational Challenges
- Bias mitigation and fairness standards in AI tools
- Intellectual property rights and licensing compliance
- Future trajectories of AI in software engineering
Practical Projects and Analytical Case Studies
- Utilization of prevalent AI tools in software engineering contexts
- Examination of AIASE implementations in commercial sectors
- Capstone exercise: Creation of an AI-enhanced software application
Concluding Summary and Recommended Actions
Requirements
- Proficiency in software development processes and methodologies
- Practical experience with Python programming
- Foundational understanding of machine learning principles
Target Audience
- Software developers
- Software engineers
- Technical leads and management personnel
Testimonials (1)
Shane had everything prepared well beforehand which made sure that we were able to follow up and do some hands on practice as well.
