Introduction to SDLC and Generative AI | AI-Powered Software Development – GenAInstein
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Introduction to SDLC and GenAI in SDLC

Instructor: Arun Kumar A R Instructor - Brisa Technologies

Centre for Development of Advanced Computing (C-DAC)

Course Fees: ₹4,999/-

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Starting On:
May 1, 2025

Format:
Online + Interactive Demo
Language:
English
Duration:
45 minutes
Certificate:
Digital Copy
Audience:
Bachelor’s and Master’s students

Course Overview

Discover how the traditional Software Development Life Cycle (SDLC) is being transformed with the power of Generative AI. This course introduces you to the core phases of SDLC requirements, design, development, testing, deployment, and maintenance while showcasing how GenAI tools can enhance productivity, streamline workflows, and automate key tasks. Whether you're a developer, tester, or project manager, you’ll learn how to integrate AI into modern software engineering practices and stay future-ready in the evolving tech landscape.

What You'll Learn

Understand the key stages of SDLC—requirements, design, development, testing, deployment, and maintenance.Learn how Generative AI enhances each phase of SDLC—from writing user stories to generating test cases and code snippets.Explore how tools like ChatGPT, GitHub Copilot, and AI-powered QA solutions are reshaping modern software workflows.Learn basic prompt engineering to automate code reviews, documentation, and bug fixes.Discover the measurable impact of AI in reducing development time, improving quality, and accelerating delivery cycles.Get insights into how GenAI will shape the next generation of software engineering roles and responsibilities.

Why Enroll in this Course?

Learn how traditional software processes are being revolutionized by GenAI an essential upgrade for every tech professional.

Go beyond theory with real-world examples and GenAI tools you can start using immediately in your workflow.

Designed for software engineers, testers, analysts, and managers whether you’re new to AI or already experimenting.

Discover how AI can accelerate tasks like documentation, coding, testing, and even project planning.

Gain a verified credential that showcases your readiness for next-gen software development practices.

Stay updated with how leading companies are embedding GenAI into DevOps, Agile, and CI/CD pipelines.

Course Syllabus

Overview of SDLC phases: Planning, Requirements, Design, Development, Testing, Deployment, and Maintenance.
Discussion on the role of Generative AI (GenAI) in transforming software development practices.
Include real-world case studies on successes and failures of SDLC with AI augmentation.
Students discuss impacts of GenAI on traditional software workflows and identify stages most changed by AI.
Explore history and evolution of GenAI in software engineering: from rule-based automation to modern LLMs (e.g., GPT series) and AI-assisted coding.
Cover concepts like natural language understanding, code synthesis, and automation tools. Highlight impact on productivity and innovation.
Detailed explanation of roles (analyst, developer, tester, maintainer) and artifacts (requirements docs, design specs, test plans) in SDLC.
Discuss how GenAI can support each role.
Group activity: map AI tools to corresponding SDLC phases and roles.
Students install and configure essential tools for the course: Python, Git/GitHub, text editors (VS Code), Jupyter Notebooks/Colab, and GenAI API clients (OpenAI API).
Overview of basics for project collaboration and documentation. Validate environment with simple setup scripts.
Task: Students research one GenAI tool (ex: GitHub Copilot, ChatGPT) and prepare a one-page summary outlining features, pros/cons, and integration in SDLC.
Optional: set up personal GitHub repos for code sharing and version control practice.
Hands-on introduction to AI-powered project management tools (Jira AI, Notion AI).
Practice creating user stories, sprint backlogs, and task boards integrating AI assistance.
Discussion of ethical considerations in AI task automation.
Overview of popular GenAI platforms/APIs (OpenAI, LangChain, GitHub Copilot, Cursor.ai, Windsurf).
Students explore documentation, authentication setup, and basic API usage examples.
Guided setup of Python/JavaScript environments to interact with GenAI APIs.
Write scripts to send minimal queries and retrieve generated responses.
Practical tips on debugging and handling API limits/errors.
Task: Students experiment with simple GenAI prompts for code generation, debugging, or documentation.
Document findings and best prompt engineering practices in shared notebooks or repos.
Explore project planning processes in SDLC.
Use GenAI tools to automate requirements capture via NLP techniques: user stories, sentiment analysis, conflict detection.
Hands-on project: create Gantt charts and resource plans augmented with AI insights.
Guide on integrating GenAI to generate, review, and refine requirements documentation.
Exercise: authorship of sample requirements with AI-generated outlines and enhancements.
Discuss ethical data handling in requirements.
Use AI tools to assess project feasibility and identify risks.
Students simulate risk assessments augmented by AI-generated reports.
Discussion of how AI can help in early detection of project pitfalls.
Setup and configuration of GenAI-powered plugins/extensions for popular planning software (e.g., JIRA, MS Project).
Practice automating sprint planning and risk documentation.
Task: Students research and prepare a comparative review of two GenAI tools for requirement elicitation/planning phases, with pros and cons, real application scenarios.
Study use of GenAI tools (Figma AI, Uizard) for UI/UX design and prototyping.
Practical session on generating wireframes and design mockups using AI assistance.
Explore how GenAI can produce and validate architecture diagrams from textual designs.
Demo using example UML and flowcharts with AI tools.
Cover AI techniques to enhance accessibility: automated testing for WCAG compliance, voice UI prototyping, and AI-powered user feedback analysis.
Hands-on session generating design documents and diagrams from project descriptions using GenAI tooling integrated with VS Code or web platforms.
Task: Students create a mini design document for a sample app using GenAI tools, then peer review and refine based on feedback.
Discuss coding practices in modern SDLC; how GenAI assists code autocomplete, generation, refactoring, and debugging.
Students learn best practices integrating AI helpers responsibly.
Demonstrate AI-based code assistance with GitHub Copilot and others.
Students get hands-on experiences writing code with autocomplete, code suggestions, and explanation features.
Address AI-assisted generation of secure code, detecting vulnerabilities, and avoiding bias.
Case studies of AI code introducing bugs and mitigations.
Students practice coding tasks with AI assistance in Python/JavaScript.
Debug and refactor generated code.
Document learnings and become familiar with prompt engineering for coding tasks.
Task: Explore AI-generated code quality on independent small projects; write reflection notes and create GitHub repos showcasing experiments.
Introduction to automated test case generation using GenAI tools such as ChatGPT, LangChain.
Discuss benefits and limitations in unit, integration, and UI testing automation.
Explore AI techniques for bug triaging and summarizing issue reports.
Practice writing natural language descriptions of bugs and generating debugging steps.
Introduction to Selenium automation integrated with GenAI-generated test scripts for UI testing workflows.
Hands-on generation and execution of AI-generated Selenium scripts.
Students build a test automation pipeline using AI-assisted test generation, run tests, analyze failures, and generate reports in Jupyter/Colab or IDEs.
Task: Research recent academic or industry papers on AI-enhanced software testing or QA techniques; present summaries and discuss applications.
Deep dive into AI coding assistants like GitHub Copilot, Windsurf, Cursor.ai, and AWS CodeWhisperer.
Explore features such as contextual code completion, intelligent refactoring, and auto-documentation
Discuss integration in IDEs (VS Code, JetBrains).
Hands-on: practice complex multi-file scaffolding with AI support.
Explore techniques for generating secure code with GenAI, identifying common risks like injection flaws or insecure defaults.
Case discussions on prevention of bias and ethical pitfalls in AI-generated code.
Practice: Static analysis tools combined with AI models to review generated code.
Cover AI-supported coding in Python, JavaScript, and Java. Comparative review of AI support and challenges per language
Hands-on: multilingual coding exercises generating modules, APIs, and tests with AI help. Students reflect on language nuances.
Hands-on application of GenAI for automatic debugging tips, error explanation, and code refactoring.
Students experiment with prompt engineering to improve code maintainability.
Pair programming and peer review to reinforce concepts.
Task: Setup personal project repos integrating AI-assistants.
Students document experiences using AI for code writing and debugging.
tart building a project that will evolve throughout the course with GenAI support logged via commits and issues.
Overview of using GenAI tools to generate unit, integration, and regression test cases automatically.
Hands-on prompts for crafting effective test scenarios with ChatGPT and LangChain frameworks.
Discuss limits and correctness assurance.
Techniques to automate bug triage, generating issue tickets, and categorizing bugs using natural language generated summaries.
Practice creating reproducible bug reports with GenAI assistance.
Integrating GenAI with Selenium for UI testing automation.
Create AI-generated Selenium scripts from textual test descriptions and refine based on test results.
Hands-on lab executing and debugging scripts. Discuss scaling automated tests in CI/CD pipelines.
Automatic generation of test coverage and results summaries, anomaly detection in test runs.
Students build basic dashboards and automate reports using LLMs and visualization libraries.
Students review recent advances in AI-powered QA/testing from papers or blogs.
Prepare short presentations summarizing innovations and practical benefits.
Review continuous integration and deployment workflows.
Explore how GenAI can assist in automating build, test, and deployment scripts.
Lab: Create basic CI/CD pipelines augmented with AI-generated shell or YAML files (GitHub Actions, Jenkins).
Introduction to Docker and Kubernetes fundamentals
Practice building container images for AI-enhanced apps.
Use AI tools to generate Dockerfiles and Kubernetes manifests with best practices for reproducibility and scalability.
Generate release notes automatically with GenAI based on commit history and issue trackers.
Discuss AI-powered monitoring tools (AIOps) for health checking and auto-remediation following deployment.
Hands-on: Setup alerts and generate incident summaries with AI.
Discuss deployment security including secrets management, vulnerability scanning, and compliance checks aided by AI code review tools
Emphasis on best practices and continuous auditing.
Case studies on failures and lessons learned.
Students create end-to-end demo deployment pipelines with AI-generated config and scripts
Document the setup, challenges, and lessons learned in shared repos/wiki pages.
Cover AI-driven concepts like self-healing systems, anomaly detection in logs, and automated patching.
Use case discussions and tools overview.
Automate incident ticket generation and prioritization through GenAI.
Hands-on: Build scripts to extract actionable reports from monitoring logs.
Introduction to building chatbots with OpenAI GPT, Dialogflow, and Rasa.
Students design and implement simple support bots for ticket routing and FAQs.
Review privacy and ethical considerations.
Use AI tools to analyze sentiment in customer interactions to prioritize support efforts.
Practice on sample chat logs and call transcripts.
Students expand on maintenance bots or monitoring scripts developed, share in project groups for feedback and collaborative improvement.
Study the importance of transparent AI-generated code and decisions.
Discuss tools and techniques for explainable AI in software engineering, including model interpretability for code suggestions and testing.
Deep dive into ethical considerations in AI-assisted software development: data privacy, bias mitigation, accountability, and governance.
Engage students in scenario-based discussions and reflections.
Usage of AI-powered Agile tools to manage team collaboration, sprint planning, and retrospectives, including automated meeting notes and action items generation.
Simulations of AI-augmented scrum ceremonies.
Implement AI-assisted code review workflows to flag bugs, style issues, and vulnerabilities.
Practice setting up automated reviews and interpreting AI feedback in pull requests.
Students investigate emerging GenAI developments in SDLC, produce reports on promising tools or academic research, and lead discussions on future impacts.
Teams finalize project topics applying GenAI to an SDLC phase.
Write detailed proposals with objectives, milestones, and deliverables.
Instructor-led guidance provided.
Conduct research on related solutions and datasets.
Evaluate feasibility and risks.
Prepare documentation.
Configure project environments: repos, CI/CD pipelines, API keys, and necessary software tools.
Setup performance and logging solutions from the start.
Begin core development with GenAI tools
Initial code commits and integration with AI assistants.
Begin first test cases and peer review cycles.
Conduct sprint planning, daily stand-ups, and review sessions with AI-generated agendas and summary notes.
Collect early feedback and iterate project scope.
Continue iterative project development, modelling, integration, and testing.
Emphasize documentation and AI-assisted code quality improvements
Perform thorough testing: unit, integration, and user acceptance tests, fix issues using AI tools
Analyze test results, system performance.
Finalize deployment scripts, environment specs, and demo presentations.
Practice storytelling and technical demos for non-technical stakeholders.
Workshops on presentation skills, conflict resolution, stakeholder communication, and team management.
Role-play scenarios and peer feedback sessions.
Encourage experimentation with novel GenAI tools or integration methods related to the capstone project.
Prepare short reflection papers or demos to present.
Complete project coding, comprehensive documentation, test scripts, and deployment configurations.
Ensure reproducibility of all results and demos.
Conduct dry runs of final presentations with peer and instructor feedback; refine slides, demos, and Q&A preparedness.
Guidance on building AI/SDLC-related resumes, LinkedIn profiles, and job interview preparation.
Conduct mock technical interviews and coding challenges.
Best practices for writing technical reports, papers, and open-source documentation
Create blogs or GitHub READMEs summarizing key project learnings.
Group reflection on course journey, lessons learned, and pathways for further study or open-source/community contributions. Gather course feedback.
Formal presentations and demonstrations to faculty and peers.
Q&A sessions and evaluation based on technical merit, innovation, and clarity.
Review of core topics, key insights on GenAI in SDLC, and student achievements.
Discussion of ongoing trends and career opportunities.
Invited industry expert(s) share experiences using GenAI in real-world software projects.
Panel Q&A on career, skills, and future directions.
Final course assessments: coding challenges, written reflections or quizzes evaluating comprehension and application of GenAI in SDLC.
Collect feedback for course improvements, distribute certificates, and provide guidance on advanced learning resources like advanced AI courses, open-source contributions, or AI competitions.
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Instructor Profile

Arun Kumar A R
Arun Kumar A R

AI & ML | Data Science & Analytics | AI-ML Model Building | Generative AI | Corporate Trainer

 

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Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.
RM
Rahul M.
Software Engineer
As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!
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Sneha K.
Final Year Student
What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.
AS
Arjun S.
Developer
The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.
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Priya D.
Tech Lead

Certification Details

Free Access: Learn at no cost
Optional Certification Exam: ₹499/-
Certification Criteria: Minimum 75% score required
Certificate Type: Digital e-certificate (no hard copies)
Exam Date: To be announced after course completion.

Certificate Sample