Role-Based Programme
RB1481

AI-Powered Software Development & Engineering

Smarter Coding, Testing, Documentation & Delivery

IMAGE REQUIRED
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Software Development and Engineering across requirements interpretation, coding, debugging, testing, documentation, and delivery.
  • Apply AI-assisted techniques to generate code drafts, explain unfamiliar code, refactor existing logic, and accelerate routine development activities.
  • Use structured prompting for code generation, debugging, test creation, documentation, review, and technical problem solving.
  • Explore AI-supported approaches for identifying defects, improving code quality, reducing repetitive work, and accelerating engineering workflows.
  • Build responsible AI-assisted development practices while maintaining security, code quality, intellectual-property protection, technical validation, and human oversight.

Tools covered

Generative AI AssistantsAI Coding AssistantsAI Search & ResearchCode Explanation & Refactoring SupportTest Generation AIDocumentation AIDebugging SupportSoftware Engineering Workflow Automation

Who should attend

  • Software Developers
  • Software Engineers
  • Application Developers
  • Frontend Developers
  • Backend Developers
  • Full-Stack Developers
  • Web Developers
  • Mobile Application Developers
  • Technical Leads
  • Development Engineers
  • Junior Software Engineers
  • Application Engineers
  • Engineering Managers
  • Software Development Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software development or programming concepts
  • Familiarity with at least one programming language is helpful
  • Basic understanding of application logic, testing, and software-development workflows
  • No previous AI knowledge required
  • No previous AI training required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to software engineering
  • Identifying AI applications across coding, debugging, testing, documentation, and code review
  • Understanding AI assistance versus Software Engineer judgement and accountability
  • Recognising limitations such as incorrect code, hallucinated APIs, outdated libraries, and insecure recommendations
Practical activities
  • Mapping a typical software-development lifecycle
  • Identifying repetitive and information-intensive engineering tasks suitable for AI assistance
  • Comparing a traditional development task with an AI-assisted approach

Scenarios

Business Requirement to Tested Code Component

Requirement → AI-Assisted Technical Breakdown → Pseudocode → Code Draft → Developer Review → Test Cases → Validation → Documentation

Participants use AI to convert a sample requirement into a draft code component, review the generated logic, create tests, and prepare supporting documentation.

Production Defect to Validated Code Fix

Reported Defect → Error Evidence → AI-Assisted Debugging → Possible Causes → Code Fix Options → Developer Validation → Regression Tests → Change Summary

Participants use AI to analyse a sample software defect, structure the debugging process, evaluate potential fixes, and prepare a validated change for engineering review.

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