Role-Based Programme
RB1482

AI-Powered Software Development & Engineering

Smarter Coding, Design, Testing & Developer Productivity

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Software Development and Engineering activities including requirements interpretation, design, coding, testing, debugging, documentation, and code review.
  • Use AI-assisted techniques to generate, explain, improve, and validate software code while maintaining engineering standards and human oversight.
  • Develop structured workflows for feature development, defect resolution, refactoring, technical documentation, testing, and release readiness.
  • Improve developer productivity through AI-assisted code analysis, reusable patterns, test generation, debugging support, and documentation.
  • Apply responsible AI practices covering secure coding, intellectual property, confidential source code, output validation, dependency risk, and human review.

Tools covered

Generative AI AssistantsCode Generation SupportCode Review AssistanceSoftware Design SupportDocumentation AssistanceTest Generation SupportDebugging SupportRequirements AnalysisDeveloper Productivity AnalyticsWorkflow Automation

Who should attend

  • Software Developers
  • Software Engineers
  • Application Developers
  • Full-Stack Developers
  • Backend Developers
  • Frontend Developers
  • Technical Leads
  • Development Leads
  • Solution Developers
  • Application Engineers
  • Software Architects
  • Platform Developers
  • Engineering Managers
  • Information Technology Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of software development, application engineering, or programming
  • Familiarity with at least one programming language and software-development lifecycle concepts
  • Basic understanding of version control, testing, APIs, or application architecture is helpful
  • Basic awareness of Generative AI is helpful
  • No advanced AI or machine-learning knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, code assistants, and their role in software engineering
  • Identifying AI applications across requirements, design, development, testing, debugging, and documentation
  • Understanding AI assistance versus developer accountability and engineering judgement
  • Recognising risks related to insecure code, hallucinated APIs, confidential code, and unverified dependencies
Practical activities
  • Mapping the software-development lifecycle to AI-assisted activities
  • Identifying repetitive development tasks suitable for AI support
  • Comparing traditional and AI-assisted engineering workflows

Scenarios

Feature Requirement to Tested Implementation

Business Requirement → AI-Assisted Technical Breakdown → Design → Code Draft → Code Review → Unit Tests → Debugging → Documentation → Ready-for-Review Build

Participants convert a simulated feature requirement into a structured technical implementation, use AI to accelerate coding and testing, and validate the output through review, debugging, and documentation.

Recurring Software Defects to Engineering Improvement Plan

Defect Data + Code Review Findings + Test Results + Build Failures → AI Analysis → Recurring Engineering Issues → Quality Gaps → Improvement Actions → Team Engineering Report

Participants consolidate software-engineering information, identify recurring defect and development patterns, and prepare a team-level improvement plan covering code quality, testing, review, and developer productivity.

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