Role-Based Programme
RB1483

AI-Powered Software Development & Engineering

Intelligent Coding, Architecture, Testing & Engineering Automation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across software design, coding, debugging, testing, documentation, code review, maintenance, and engineering workflows.
  • Use AI to analyse requirements, codebases, APIs, logs, defects, architecture information, test results, and technical documentation.
  • Build repeatable AI-assisted workflows for code generation, refactoring, debugging, test creation, documentation, peer review, and engineering automation.
  • Apply AI to identify code-quality issues, recurring defects, maintainability risks, architecture concerns, testing gaps, and developer-productivity opportunities.
  • Design responsible AI-enabled software engineering workflows with appropriate controls for code security, intellectual property, data privacy, validation, testing, change management, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchCoding AssistantsCode Review ToolsSoftware Architecture ToolsAPI Development ToolsAutomated Testing ToolsDebugging & Log AnalysisDocumentation AIDevOps & CI/CD ToolsWorkflow Automation

Who should attend

  • Software Developers
  • Software Engineers
  • Senior Software Engineers
  • Application Developers
  • Full-Stack Developers
  • Backend Developers
  • Frontend Developers
  • API Developers
  • Technical Leads
  • Engineering Leads
  • Software Architects
  • Development Managers
  • Platform Developers
  • Product Engineering Professionals
  • Software Development Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of software development, programming, application engineering, or solution development
  • Familiarity with source code, APIs, debugging, testing, version control, and software development lifecycles
  • Basic proficiency with development environments, technical documentation, and software engineering tools
  • No previous AI course attendance required
  • Basic programming knowledge recommended

TOC Modules

Concepts
  • Understanding Generative AI, coding assistants, analytical AI, and engineering automation
  • Mapping AI opportunities across the software development lifecycle
  • Understanding AI assistance versus accountable engineering decisions
  • Recognising hallucinated code, security, licensing, privacy, and reliability risks
Practical activities
  • Mapping an existing software engineering workflow
  • Comparing manual and AI-assisted development activities
  • Creating a Software Engineering AI opportunity matrix

Scenarios

Requirement to Production-Ready Feature

Business Requirement → Technical Analysis → Architecture → AI-Assisted Coding → Unit Tests → Code Review → Integration Testing → Documentation → Release Readiness

Participants use AI-assisted engineering techniques to convert a simulated business requirement into a tested and documented software feature while applying appropriate code review, security, and validation controls.

Legacy Application to AI-Assisted Modernisation Workflow

Legacy Code + Defect History + Architecture + Test Coverage + Technical Debt → AI Analysis → Modernisation Opportunities → Refactoring Plan → Automated Tests → Controlled Changes → Engineering Dashboard → Technical Review

Participants design an AI-enabled software engineering workflow that analyses legacy application information, prioritises technical debt, supports safe refactoring and testing, and improves engineering visibility while retaining final design and production decisions with authorised engineering professionals.

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