Intelligent Engineering with AI

Originally posted on Dev3loper.ai

Imagine supercharging your software development workflow with the power of artificial intelligence. As the founder of Dev3l Solutions and a Staff Engineer at Artium, I've spent years integrating AI into production systems—creating innovative solutions such as RAG systems for clients and enhancing personal projects. These experiences have demonstrated the transformative potential of AI in real-world applications.

On the other hand, tools like GitHub Copilot and ChatGPT have become indispensable for daily software development tasks. These AI tools streamline coding, provide intelligent suggestions, and assist with debugging, greatly enhancing efficiency and code quality. Recently, I partnered with LeanDog to create and instruct the "Intelligent Engineering with AI" course, aimed at sharing these groundbreaking techniques with fellow developers.

Integrating AI with traditional software development practices is not just a trend but a crucial evolution. AI tools can automate repetitive tasks, provide intelligent code suggestions, and assist in debugging, significantly reducing development time and improving code quality. Fusing AI and traditional methodologies fosters innovation, enhances productivity, and ensures developers can focus on more complex problem-solving aspects. This course encapsulates the essence of blending AI with tried-and-true development techniques, showcasing its potential to elevate coding standards and efficiency.

The practice problems and course materials can be found here.

AI Tools Integration

We started the course by exploring integrating AI tools into our workflow, emphasizing how indispensable they can become. GitHub Copilot and GitHub Copilot Chat were stars of the show, uncovering how they transcend beyond simple code generation. We delved into their advanced features, like real-time autocompletion and debugging assistance, which make coding faster and more intuitive.

Participants were particularly impressed with how GitHub Copilot could swiftly generate code snippets, eliminating the monotony of writing boilerplate code. This has saved considerable time in my projects, allowing me to focus on complex problem-solving rather than repetitive tasks. The tool's intelligent autocompletion capabilities were another game-changer, offering suggestions that save time and minimize potential errors in the early stages of development. Those unfamiliar with the C# programming language could quickly write functional code. Participants had no problem completing the exercises, thanks to AI assistance. Regarding debugging, GitHub Copilot provides invaluable assistance by identifying issues, suggesting fixes, and streamlining the coding process.

We didn't stop there. ChatGPT demonstrated its prowess in significantly enhancing productivity. It can generate detailed code documentation and provide real-time coding advice, and it also excels in creating diagrams with Mermaid or Graphviz. These visual aids are crucial for understanding and communicating complex system designs. Imagine having an AI partner that can produce clear, concise diagrams right when you need them!

Moreover, we explored how both GitHub Copilot and ChatGPT serve as virtual pair programming partners. They prove invaluable for suggested refactorings, providing insights into making the code cleaner and more efficient. They also assist in code reviews, ensuring the code adheres to best practices and maintaining high quality.

A unique aspect of our course was the introduction of a custom GPT I developed, named Tyler Morgan, who acted as a virtual course assistant. Tyler Morgan offered insights and strategies for integrating AI tools in software engineering, including coding practices, agile methodologies, and team collaboration. Students and anyone interested can access Tyler anytime!

Throughout the course, participants were encouraged to get hands-on and leverage these AI tools while working on all the practice problems. This practical approach ensured that everyone could experience firsthand how these tools boost productivity and enhance the overall quality of their code. By using these tools as intelligent collaborators, developers can focus more on creative and complex aspects of software development.

Test-Driven Development (TDD)

In the course, we devoted much time to mastering Test-Driven Development (TDD), a cornerstone of reliable software engineering. Understanding the core principles of TDD was paramount, beginning with the foundational Red, Green, Refactor cycle. In this approach:

• Red: You start by writing a test that fails because the desired feature isn't implemented yet.
• Green: Next, you write the minimal amount of code needed to pass the test.
• Refactor: Finally, you clean up the code, optimizing it without altering its behavior.

This cycle encourages simplicity and regular refinement, which is essential for maintaining clean and efficient code.

We emphasized the importance of TDD in ensuring code reliability and maintainability. The tests act as a safety net, catching bugs early and giving developers the confidence to make changes without fear of breaking existing functionality. This continuous testing approach reduces the likelihood of defects and makes the codebase easier to understand and modify. With the assistance of AI tools, TDD becomes even more powerful, as they can provide intelligent code suggestions while ensuring that these suggestions do not cause any regressions. This synergy between TDD and AI ensures a robust, high-quality codebase.

To make these concepts tangible, we dove into several practical katas:

• Fizz Buzz: This classic exercise introduced participants to TDD basics, establishing a solid foundation.
• Duration Converter: We practiced converting between different time units, reinforcing how TDD can handle various transformations and validations.
• Bowling Kata: This problem required managing a complex scoring system with numerous edge cases, demonstrating TDD's power in handling intricate logic.
• Roman Numeral Calculator: Participants converted numbers into Roman numerals, sharpening their algorithmic thinking and ensuring correctness through tests.
• Gilded Rose Kata: Perhaps the most intricate kata, this exercise involved maintaining and refactoring a legacy codebase. It highlighted how TDD can help add new features and improve existing systems for better design and performance.

Participants were encouraged to collaborate and pair up to solve these katas, fostering a shared learning experience. Leveraging AI tools like GitHub Copilot and ChatGPT, they wrote tests, refactored code, and saw the immediate benefits of having a robust testing strategy. This hands-on approach allowed everyone to experience the efficiency and quality improvements TDD brings.

The practical insights shared during these exercises were directly applicable to real-life projects. We discussed common challenges, such as integrating TDD into existing workflows and dealing with initially slow development due to writing tests upfront. However, the long-term benefits, such as continuous validation of code functionality and early detection of issues, far outweigh the initial overhead.

By the end of this section, participants recognized that TDD is not just a testing technique but a development methodology that enhances code quality and developer confidence. It provides a safe environment to refactor code, ensuring functionality remains intact and paving the way for more innovative and bold coding endeavors.

Software Craftsmanship

We have dedicated a substantial segment to software craftsmanship. This philosophy goes beyond just writing functional code; it emphasizes writing clean, maintainable, and efficient code that can withstand the test of time. It's about professional pride, continuous learning, and striving for excellence in every line of code we write.

We began by introducing the concept of Software Craftsmanship. The idea is to go beyond mere functionality and focus on building high-quality software. Taking pride in our work and continually honing our skills are essential tenets. This approach not only elevates the quality of the code but also increases overall developer satisfaction and team productivity.

Alongside design patterns, we delved into software design principles:

• SOLID Principles offered a robust framework:
  • Single Responsibility Principle (SRP) encourages designing classes with only one reason to change, which enhances modularity and readability.
  • Open/Closed Principle (OCP) promotes the idea that software entities should be open for extension but closed for modification, fostering a more adaptable codebase.
  • Liskov Substitution Principle (LSP) asserts that objects of a superclass should be replaceable with objects of a subclass without affecting functionality, ensuring reliable and stable code.
  • Interface Segregation Principle (ISP) advocates for creating specific interfaces rather than a general-purpose one, which helps reduce unnecessary dependencies.
  • Dependency Inversion Principle (DIP) highlights that high-level modules should not depend on low-level modules. Both should depend on abstractions, lending to a more flexible and decoupled design.
• DRY (Don't Repeat Yourself) encourages abstracting out commonalities to reduce repetition, making the code more maintainable and more accessible to update.
• YAGNI (You Ain't Gonna Need It) emphasizes implementing features only when necessary, preventing overengineering and unnecessary complexity.
• Boy Scout Rule: This principle suggests that developers should always leave the codebase cleaner than they found. Just like Boy Scouts are taught to leave the campground cleaner, programmers should make minor improvements to the code whenever they touch it, ensuring continuous enhancements.
• ZOMBIES was particularly useful for problem-solving and Test-Driven Development (TDD). It's an acronym that stands for:
  • Zero: Start with the simplest thing that can work, focusing on base case scenarios.
  • One: Get one scenario to work, confirming the functionality for a single instance.
  • Many: Generalize to handle multiple cases, ensuring the solution works across variations.
  • Boundaries: Identify and define the system's boundaries.
  • Interfaces: Ensure clear and well-defined interfaces.
  • Errors: Proper handling of errors and edge cases.
  • Simple: Keep the approach simple, avoiding unnecessary complexity.

Identifying and addressing code smells was another critical aspect of our course. We pinpointed common issues such as:

• Long Methods: Methods that have grown too large and complicated.
• Large Classes: Classes taking on too many responsibilities.
• Duplicated Code: Identical code blocks appearing in multiple places.
• Feature Envy: Methods that overly rely on the details of another class.

To combat these, we introduced practical refactoring techniques:

• Extract Method: Breaking down extensive methods into smaller, more manageable pieces.
• Rename Variable: Using meaningful variable names to improve readability.
• Introduce Parameter Object: Grouping parameters into an object to streamline method signatures.
• Remove Dead Code: Cleaning out code no longer used to keep the codebase lean and efficient.

Finally, we discussed design patterns, which are proven solutions to common problems in software design. We explored critical patterns like:

• Singleton: Ensure a class has only one instance and provides a global access point. It is beneficial in scenarios requiring a single control point, like logging or configuration settings.
• Factory: Creating objects without specifying the exact class of the object that will be created. This is essential for maintaining flexibility and decoupling the code.
• Strategy: Defining a family of algorithms, encapsulating each one, and making them interchangeable. This pattern is invaluable for scenarios where multiple algorithms can be applied interchangeably.
• Observer: Establishing a one-to-many dependency between objects so that when one object changes state, all its dependents are notified, which is particularly useful in event-handling systems.
• Decorator: Dynamically attaching additional responsibilities to an object, providing a flexible alternative to subclassing for extending functionality.
• Command: Encapsulating a request as an object allows for parameterizing clients with queues, requests, and operations, which is instrumental in implementing undo/redo functionalities.

During hands-on sessions, participants were tasked with applying these design patterns and principles to existing codebases. AI tools like GitHub Copilot and ChatGPT were invaluable here, helping to identify code smells quickly and suggest ways to refactor them.

By focusing on Software Craftsmanship, participants recognized the immense benefits:

• Enhanced Code Quality: Resulting in cleaner, more efficient, and maintainable code.
• Sustainable Development: Making the codebase more straightforward to manage and extend over time.
• Improved Team Collaboration: Ensuring a shared understanding and maintaining high standards among all team members.

Hands-On Exercises and Practical Applications

The end of the course focused on hands-on exercises, which were essential for ensuring participants could apply what they learned in real-world scenarios. By actively engaging with AI tools like GitHub Copilot and ChatGPT, participants gained practical experience and confidence in integrating these technologies into their workflows.

We emphasized prompt engineering throughout the course, as it is crucial for effectively leveraging AI capabilities. Participants learned what makes a good prompt, how to write effective prompts, and different styles of prompts to meet various needs. This continuous practice ensured that participants could maximize the potential of AI tools, tailoring them to specific tasks and challenges.

Next, we tackled the Task API project. This pre-built mini-system allowed participants to practice their TDD and AI skills on a more complicated project than simple katas. The goal was to add a new feature to the system, using TDD/AI, providing practical experience in a realistic setting. The project contained examples of:

• Controller Tests using a Test Client: Demonstrating how to structure tests for API controllers.
• Mocking: Simulating interactions with dependencies to test isolated components.
• Managing Data through Migrations: Handling database schema changes effectively.
• Creating Idempotent Tests for Database Interactions: Ensuring tests remain reliable and repeatable, even with database changes.

With GitHub Copilot assisting in generating code snippets and offering suggestions for enhancing code quality, participants could focus on implementing new features efficiently. ChatGPT provided real-time coding advice and debugging assistance, further streamlining the development process. This hands-on project illustrated how AI tools could integrate into more complex development tasks, not just simple exercises.

We also emphasized collaborative coding exercises, such as pair programming. Participants worked in pairs to solve problems, share knowledge, and develop strategies. AI tools enhanced this collaborative approach by acting as virtual pair programmers and code reviewers, providing real-time feedback and improvements.

By the end of the course, participants were not only theoretically versed in the integration of AI tools but also practically equipped to enhance their software development processes. This hands-on experience ensured the lessons learned could be directly applied, paving the way for more innovative, efficient coding practices.

Continuous Integration/Continuous Deployment

In our course, we dedicated much of our time to mastering continuous integration and continuous deployment (CI/CD), with valuable assistance from AI tools. CI/CD is crucial in modern software development, streamlining workflows and reducing errors while ensuring continuous feedback and high-quality code through automation.

We introduced GitHub Actions, a powerful and versatile tool for CI/CD pipelines. GitHub Actions integrates seamlessly with existing repositories, enhancing productivity and maintaining code quality. Participants quickly saw this tool's potential as they set up their CI/CD pipelines. With GitHub Copilot and ChatGPT, they navigated the complexities of CI/CD effortlessly.

One of the hands-on projects involved creating a complete CI/CD pipeline using GitHub Actions. AI tools meticulously guided this process, offering real-time code suggestions and troubleshooting tips. Participants defined workflow files using GitHub Copilot, which generated YAML files outlining different CI/CD stages. They then incorporated automated tests to ensure code quality, built and packaged applications, and automated deployment to environments such as staging and production. The presence of AI, particularly ChatGPT, and our custom GPT, Tyler Morgan, was instrumental in providing detailed insights and solving issues on the fly.

The practical session of setting up a project repository and configuring initial settings offered a tangible experience. With AI assistance, participants created workflow YAML files and ran initial builds and tests, witnessing the efficiency of automated processes firsthand. ChatGPT and Tyler provided the necessary support, ensuring everything ran smoothly and any roadblocks were swiftly addressed.

Throughout the course, we emphasized the many benefits of CI/CD. Participants experienced how CI/CD, enhanced by AI, creates a continuous feedback loop, offering timely insights on code changes and helping identify and address issues early. They saw how automating repetitive tasks with AI tools accelerated development cycles, fostering rapid iterations and improving code quality through consistent automated testing and validation. Simplified deployment processes, achieved with minimal manual intervention, reduced the risk of errors, and streamlined development efforts.

We didn't stop there. The course also covered advanced CI/CD topics, exploring how AI tools could further enhance these processes. Participants learned about automating more complex scenarios and intelligent error detection, integrating security checks into CI/CD pipelines, and ensuring compliance with industry standards and regulations.

Key takeaways from this section included best practices for setting up and maintaining CI/CD pipelines, strategies for scaling CI/CD workflows for larger teams and complex projects, and discussions on emerging trends in CI/CD that could shape the future of software development.

Emergent Design and Legacy Code

We concluded with a critical discussion on emergent design and handling legacy code. These are often the most challenging yet rewarding aspects of software development. Emergent design emphasizes incrementally evolving your software architecture, keeping it adaptable and agile as the project grows and changes.

We started by introducing the concept of emergent design and highlighting its importance in maintaining software systems' flexibility and responsiveness. Instead of fully defining the architecture upfront, emergent design allows it to evolve naturally as new requirements emerge. This approach is particularly beneficial in dynamic environments where requirements frequently change, ensuring the software remains relevant and practical.

Vital to understanding emergent design are Kent Beck's simple design principles. We outlined these principles as:

• Runs all tests: Prioritizing a test suite that verifies the correctness of the system.
• Contains no duplication: Encouraging the elimination of redundant code to maintain simplicity and reduce bloat.
• Expresses the intent of the programmer: Writing code that is clear and understandable, reflecting the underlying purpose.
• Minimizes the number of classes and methods: Keeping the codebase lean and manageable by avoiding unnecessary complexity.

Implementing these principles in real projects can be transformative. Participants learned practical strategies for applying these principles, ensuring their code remains clean, resilient, and easy to modify.

We then tackled the perennial challenge of legacy code. Legacy systems are often outdated, complex, and challenging to maintain. We discussed common issues with legacy codebases and the daunting task of maintaining and enhancing old code. The key is to improve these systems incrementally without introducing new errors or breaking existing functionality.

Participants were introduced to techniques for safely refactoring legacy code. One effective strategy is the "Strangler Fig" pattern, which involves gradually replacing parts of the legacy system with new functionality. This method allows continuous improvement without a complete system overhaul, minimizing disruptions and spreading the workload.

Our hands-on sessions provided practical insights into refactoring legacy codebases. We walked through a step-by-step guide to refactoring a legacy system, demonstrating how to improve structure, readability, and maintainability. AI tools like GitHub Copilot and ChatGPT were invaluable here, assisting in identifying problem areas and suggesting effective refactoring tactics. These tools also helped ensure that any changes were safe and didn't introduce new issues.

We wrapped up this segment by discussing the overarching benefits of adopting emergent design and effectively managing legacy code:

• Maintaining System Agility: An adaptable codebase can more easily accommodate new requirements and changes.
• Improved Code Quality: Consistently applying refactoring techniques enhances system reliability and readability.
• Legacy Systems Revival: By transforming outdated, complex systems into manageable codebases, organizations can extend the life and value of their software.

Key takeaways from this section included gaining practical skills in refactoring and improving legacy systems, with a strong emphasis on continuous code and design enhancement. By the end of the course, participants recognized the critical role of emergent design and effective legacy code strategies in maintaining high-quality, sustainable software projects.

Conclusion

As we wrapped up the "Intelligent Engineering with AI" course, it was clear that integrating AI tools into traditional software development practices is an intriguing possibility and a game-changing reality. This course journeyed through the transformative power of AI in every aspect of software engineering, from coding efficiencies to maintaining legacy systems, highlighting how these technologies can elevate individual and team productivity and code quality.

Starting with the profound capabilities of AI tools like GitHub Copilot and ChatGPT, we saw how these assistants could supercharge daily tasks. They automate code generation and debugging and act as intelligent collaborators, significantly reducing the time spent on repetitive tasks and enhancing precision and efficiency. Participants were empowered to harness these tools effectively, realizing how integral they can become in modern development workflows.

The course demonstrated the practical benefits of integrating AI with Test-Driven Development (TDD) through hands-on projects and real-world applications. Writing clean, reliable code became more manageable and intuitive, with AI tools guiding and supporting the process. By tackling exercises like the Fizz Buzz, Bowling Kata, and Gilded Rose, participants experienced firsthand the power of combining AI assistance with TDD principles to create robust and maintainable codebases.

The exploration of software craftsmanship underscored the importance of writing functional, elegant, and sustainable code. Design patterns like Singleton and Factory and principles like SOLID and DRY became part of the participants' toolkits, allowing them to craft code efficiently and proficiently. The focus on identifying and refactoring code smells, with AI assistance, further cemented the practice of continuous improvement and high standards.

Our deep dive into CI/CD processes, augmented by AI tools, revealed how automation can revolutionize development cycles. Setting up pipelines with GitHub Actions, participants automated testing, building, and deployment, streamlining their workflows and ensuring quick, reliable feedback on code changes. This practical knowledge positioned them to implement and optimize CI/CD pipelines in their projects, backed by the support of AI for even more efficient automation.

Finally, tackling emergent design and legacy code brought everything full circle. By learning to manage and improve legacy systems using techniques like the "Strangler Fig" pattern and Kent Beck's simple design principles, participants could see how even the most challenging aspects of software development could be approached methodically and effectively. AI tools played a crucial role in this process, providing insights and refactoring solutions that simplified and enhanced the task of maintaining system agility and code quality.

The essence of this course lies in the perfect harmony between human ingenuity and AI assistance. By embracing AI tools, the participants increased their productivity and significantly enhanced the quality and maintainability of their code. This course was not just an educational experience but a look into the future of software engineering, where AI and human creativity work side by side.

As we look ahead, thinking about the limitless possibilities is exciting. The skills and knowledge gained here are just the beginning. Whether it's writing new applications, refactoring old ones, or setting up sophisticated CI/CD workflows, the future of software development is brighter and more innovative with AI. The course has armed participants with the tools and insights to lead this exciting journey.

Thank you for joining this exploration of AI in software development. Together, we're paving the way for more intelligent, efficient, and creative engineering solutions. Here's to the future of clever engineering!