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⇱ Generative AI Tools for Modern Software Engineering | Coursera


Generative AI Tools for Modern Software Engineering

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Generative AI Tools for Modern Software Engineering

Included with

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Learn more

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Navigate and explore codebases using AI tools like Cursor AI, CodeSee, and Sourcegraph efficiently.

  • Improve code quality with automated reviews, static analysis, and bug detection via AI-powered tools.

  • Generate, refactor, and debug code quickly using AI assistants like Codeium, Refact AI, and Cody AI.

  • Enhance security, optimize performance, and boost collaboration with AI-driven development practices.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignmentsΒΉ

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Generative AI for Software Engineers & Developers Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This program offers a structured journey into the transformative world of AI-powered code understanding, quality assurance, and intelligent development workflows. Designed for developers, software engineers, and technical leads, this course empowers you to leverage cutting-edge AI tools for efficient code navigation, review, debugging, security, and optimization.

By the end of this program, you will be able to: - Analyze and explore large codebases quickly with AI tools for faster understanding and onboarding. - Review and evaluate code automatically to ensure high-quality, reliable, and maintainable software. - Create, refactor, and debug code efficiently using intelligent AI-powered assistants. - Secure applications by detecting vulnerabilities, managing dependencies, and enhancing code safety. - Optimize and improve performance with AI-driven profiling, tuning, and resource management tools. This program is ideal for software engineers, AI professionals, and tech leads aiming to enhance their coding workflows with AI. A foundational understanding of programming concepts, version control, and basic software development practices is recommended. Join us to unlock the power of AI in software engineering and transform the way you navigate, build, and maintain code.

This module explores AI-powered tools for code navigation, understanding, and quality improvement. Learners gain hands-on experience with tools like Cursor AI, CodeSee, Sourcegraph, and Qodo to analyze codebases, perform reviews, detect issues early, and enhance software reliability.

What's included

17 videos5 readings3 assignments2 discussion prompts

17 videosβ€’Total 94 minutes
  • Specialization Introductionβ€’7 minutes
  • Course Introductionβ€’6 minutes
  • Introduction to Code Navigation with AI: The Big Pictureβ€’5 minutes
  • Deep Dive with Cursor AI: Context-Aware Explorationβ€’6 minutes
  • Demonstration: Cursor AI Installation and Workingβ€’6 minutes
  • Demonstration: Building Interactive UI with Cursor AIβ€’5 minutes
  • Code Visualization with CodeSeeβ€’4 minutes
  • Code Navigation with Sourcegraphβ€’6 minutes
  • Rewind.ai for Code Tracing and Understanding Historyβ€’5 minutes
  • AI-Powered Code Review: Introduction to Qodoβ€’7 minutes
  • Automated Testing Using Qodoβ€’5 minutes
  • Qodo: Code Review Workflowβ€’4 minutes
  • Demonstration: Getting Started with Qudo Genβ€’4 minutes
  • Demonstration: Qodo Gen Chat with WeatherApp Codeβ€’5 minutes
  • Code Review with Codepeerβ€’6 minutes
  • Snyk for Static Analysis Detecting Issues Earlyβ€’7 minutes
  • CodeRabbit - Speed Up Code Reviews & Minimize Bugsβ€’6 minutes
5 readingsβ€’Total 75 minutes
  • Course Overviewβ€’15 minutes
  • Foundations of AI-Assisted Code Comprehensionβ€’15 minutes
  • Best Practices for Code Search and Query Designβ€’15 minutes
  • Principles of Automated Code Review in AI Systemsβ€’15 minutes
  • Module Summary: AI for Code Understanding and Qualityβ€’15 minutes
3 assignmentsβ€’Total 42 minutes
  • Practice Quiz: Code Navigation and Understanding with AIβ€’6 minutes
  • Practice Quiz: Code Review and Quality Improvement with AIβ€’6 minutes
  • Knowledge Check: AI for Code Understanding and Qualityβ€’30 minutes
2 discussion promptsβ€’Total 10 minutes
  • Introduce Yourselfβ€’5 minutes
  • From Static Analysis to Smart Automationβ€’5 minutes

This module explores AI-powered code creation and debugging, focusing on intelligent code generation, optimization, and problem-solving. Learners will work with tools like Codeium, Refact AI, Trae, and Cody AI to write efficient code, automate refactoring, and enhance debugging processes. The module also covers ethics, reliability in AI-generated code, and practical techniques for error detection and resolution.

What's included

12 videos3 readings3 assignments2 discussion prompts

12 videosβ€’Total 59 minutes
  • Codeium: Revolutionizing Code Creation with AIβ€’4 minutes
  • Demonstration: Getting Started with Windsurf in VS Codeβ€’6 minutes
  • Crafting Better Code: The SONAR Methodβ€’6 minutes
  • Refact AI for Smarter Code Optimizationβ€’4 minutes
  • Demonstration: Refact AI Setup and Featuresβ€’6 minutes
  • Exploring Trae: The Adaptive AI IDEβ€’3 minutes
  • Demonstration: Getting Started with Trae AI Builderβ€’6 minutes
  • Introduction to AI-Assisted Debugging: The Approachβ€’5 minutes
  • Identifying and Resolving Errorsβ€’4 minutes
  • Replit Code Debuggingβ€’3 minutes
  • Demonstration: Creating and Debugging Apps with AIβ€’7 minutes
  • Cursor for Debugging: Stepping Through Codeβ€’4 minutes
3 readingsβ€’Total 45 minutes
  • Static Analysis Vs. Dynamic Testing: A Comparative Studyβ€’15 minutes
  • Ethics and Reliability in AI-Generated Codeβ€’15 minutes
  • Module Summary: AI-Powered Code Creation and Debuggingβ€’15 minutes
3 assignmentsβ€’Total 42 minutes
  • Practice Quiz: Code Generation and Assistance with AIβ€’6 minutes
  • Practice Quiz: AI-Driven Debugging and Problem Solvingβ€’6 minutes
  • Knowledge Check: AI-Powered Code Creation and Debuggingβ€’30 minutes
2 discussion promptsβ€’Total 10 minutes
  • Smarter Development with AI Assistanceβ€’5 minutes
  • Enhancing Accuracy or Creating New Risks?β€’5 minutes

This module explores AI-driven secure, optimized, and collaborative development using tools like Snyk, DeepSource, Codacy, CodeAnt, Minware, Grit.io, mabl, and Katalon. It covers vulnerability detection, secure coding, performance optimization, resource management, workflow automation, and enhanced team collaboration with AI.

What's included

15 videos5 readings4 assignments3 discussion prompts

15 videosβ€’Total 75 minutes
  • Introduction to AI and Security: Why It's Importantβ€’6 minutes
  • Dependency Scanning with Snyk: Finding Vulnerabilitiesβ€’4 minutes
  • Demonstration: Snyk Installation and Workingβ€’4 minutes
  • Detecting Code Vulnerabilities with DeepSourceβ€’4 minutes
  • Customizing Static Analysis with DeepSourceβ€’4 minutes
  • AI Tools for Security Awareness Training and Best Practicesβ€’5 minutes
  • Codacy AI for Code Performance and Optimizationβ€’6 minutes
  • AI-Powered Code Profiling with Codacyβ€’5 minutes
  • Performance Tuning with CodeAnt: Code Improvementβ€’5 minutes
  • Development Tracking with Minwareβ€’5 minutes
  • Grit.io: Your Automated Maintenance Partnerβ€’5 minutes
  • Mabl Integrating AI into Test Automationβ€’5 minutes
  • Demonstration: Automated Test Creation Using Mablβ€’5 minutes
  • Resource Planning with Minwareβ€’6 minutes
  • Katalon Smart Testing Strategiesβ€’4 minutes
5 readingsβ€’Total 75 minutes
  • Secure Coding Practices Enhanced by AIβ€’15 minutes
  • AI-Driven Profiling Techniques for High-Performance Codeβ€’15 minutes
  • Workflow Automation in Software Teams: AI's Role in CI/CDβ€’15 minutes
  • AI-Augmented Collaboration: Best Practices for Engineering Teamsβ€’15 minutes
  • Module Summary: AI for Secure, Optimized, and Collaborative Developmentβ€’15 minutes
4 assignmentsβ€’Total 48 minutes
  • Practice Quiz: AI for Security and Dependency Managementβ€’6 minutes
  • Practice Quiz: Performance Optimization and Resource Management with AIβ€’6 minutes
  • Practice Quiz: AI for Automation and Team Collaborationβ€’6 minutes
  • Knowledge Check: AI for Secure, Optimized, and Collaborative Developmentβ€’30 minutes
3 discussion promptsβ€’Total 15 minutes
  • Building a Security-First Mindset with AI Assistanceβ€’5 minutes
  • Smarter Resource Allocation with AIβ€’5 minutes
  • Balancing Automation and Human Oversightβ€’5 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz.

What's included

1 video2 assignments1 discussion prompt

1 videoβ€’Total 3 minutes
  • Course Summaryβ€’3 minutes
2 assignmentsβ€’Total 90 minutes
  • End Course Knowledge Check: Generative AI Tools for Modern Software Engineeringβ€’60 minutes
  • Designing an AI-Powered Secure Code Management and Optimization Systemβ€’30 minutes
1 discussion promptβ€’Total 5 minutes
  • Describe Your Learning Journeyβ€’5 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka
211 Coursesβ€’190,189 learners

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Frequently asked questions

This course is ideal for software developers, QA engineers, DevOps professionals, and tech enthusiasts who want to leverage AI for code navigation, review, debugging, and optimization.

You’ll learn AI-assisted code navigation, automated code reviews, debugging with AI tools, code security and dependency scanning, performance optimization, test automation, and AI-augmented team collaboration.

By the end, you’ll be able to apply AI tools for code understanding, conduct automated reviews, debug efficiently, improve code quality, detect vulnerabilities, and enhance collaboration in development workflows.

Yes! The course includes practical demonstrations and guided exercises using tools like Cursor AI, Qodo, Windsurf, Trae AI, Refact AI, Replit and more to help you learn by doing.

Most tools covered offer free tiers or trial access for hands-on practice, so you can explore without additional cost.

The course is designed for 4–5 weeks at a study pace of 4–5 hours per week. You can progress at your own speed and revisit content anytime.

Yes, after successfully completing all modules and assessments, you’ll receive a certificate validating your skills in AI-powered code understanding and quality improvement.

This course can help you grow into roles such as AI-powered developer, code quality engineer, software automation specialist, AI debugging consultant, or tech lead focusing on AI-assisted development.

Prior coding knowledge is an advantage but not essential, as the course guides you through concepts in a clear, step-by-step manner.

Generative AI in software engineering refers to AI models that can create new code, functions, or entire applications by learning from vast codebases and patterns. These tools can suggest implementations, generate boilerplate code, and even refactor existing logic, significantly accelerating the development process.

AI-powered tools can analyze project structures, visualize dependencies, and provide semantic search capabilities. This allows developers to quickly locate relevant functions, understand unfamiliar code sections, and onboard faster to complex projects.

AI-driven code review tools can detect syntax errors, security vulnerabilities, and code smells faster than manual reviews. While they don't fully replace human judgment, they significantly reduce review time and improve code quality consistency across teams.

AI debugging assistants can trace error patterns, suggest fixes, and even automatically patch certain bugs. They analyze logs, runtime behavior, and previous solutions to guide developers toward resolving issues more efficiently.

AI tools can automatically scan dependencies, identify security loopholes, and detect patterns associated with potential exploits. This proactive approach helps developers patch vulnerabilities early in the development lifecycle, improving overall application security.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.