Code Quality with AI

How AI Can Help You Write Better Code

Have you heard that AI generates “ garbage at high speed”? It’s a common concern, especially among experienced developers. But the reality is much more interesting and promising. AI can be your best ally for writing better-quality code.

🎯 The Elephant in the Room: AI Quality

The debate about AI in software development is like a roller coaster of opinions. The truth, as usual, is somewhere in the middle.

There are two opposing perceptions around the application of AI in software development:

• Excitement: They are allies that speed up tasks and improve processes.
• Skepticism: They generate complex, redundant, or difficult-to-maintain code.

The key to resolving this dichotomy is understanding that AI does not replace the developer but rather is a tool that can improve the entire development process, following the principles of AI-Driven Development.

Destroying the Myth of “Garbage Code”

The myth of “garbage code generated at high speed” has some basis when these tools are used without supervision. However, the problems do not lie in the technology but in the user’s focus.

• Poor use: Many novice developers tend to blindly trust AI suggestions.
• Lack of context: Without a good description of the problem, AI can generate generic or inappropriate code.

When used strategically and with human supervision, the code generated by AI can equal or surpass the quality of code produced manually.

💡 Beyond the Generator: The True Value of AI

When we talk about AI in programming, many people only focus on the ability to generate code. But this is like judging a book by its cover. The true power of AI lies in how it can improve the entire development process, following the principles of AI-Driven Development:

• Automating repetitive tasks
• Improving security and robustness
• Refactoring intelligently
• Adhering to quality standards
• Generating documentation
• Continuous learning

Intelligent Automation: Farewell to Tedious Tasks

Do you find yourself bored writing basic unit tests or setting up project files? Do you have the right scripts and configuration files? AI can handle these routine tasks. Imagine being able to devote that time to solving more interesting and creative problems.

AI tools can:

• Generate unit tests that actually make sense
• Create basic project structures that follow best practices
• Configure custom templates according to your specific needs

Digital Detective: Finding Errors Before They Become Problems

AI is like having an experienced programmer looking over your shoulder. It can detect subtle patterns of errors that even experienced developers might miss. Static analysis tools enhanced by AI go beyond traditional linters:

• Identify anti-patterns in real-time
• Suggest performance optimizations
• Detect security vulnerabilities

For example, they can suggest more robust solutions for API input verification or function error handling. They also remember to write logs and metrics. Or to wrap potential errors in exception handlers. The typical things you leave for later…

The Art of Refactoring

Refactoring doesn’t have to be a painful experience. AI-powered tools can analyze large codebases and suggest improvements that increase readability and maintainability.

¿How many times have you thought this if would look better inverted?, but ¡what a pain!

It’s like having an experienced mentor looking over your shoulder, but without the stress of feeling judged, and that not only suggests but also acts.

Quality Code from the Start

Maintaining consistency in a large project or a large team can be a challenge. AI can be configured to meet specific quality standards:

• Custom linters: Style and best practice rules.
• Integration of design patterns: Suggestions based on principles such as DRY, SOLID, KISS, or functional programming with immutable data, etc.
• Automated code reviews: Identify areas for improvement and suggest corrections… without the human factor that can introduce biases or carelessness.

For example, Cursor and Copilot can be adapted to the conventions of your team to maintain consistency throughout the project. See my article on assistants for more details.

Documentation That Doesn’t Give You the Blues

Admit it: nobody likes writing documentation. And it’s often one of the most neglected aspects of software development. But AI can help you:

• Generate clear and meaningful comments
• Keep technical documentation up-to-date
• Create usage guides based on existing code
• Understand legacy code

This fosters more efficient maintenance and a better understanding of the code by new developers. Or even future rewrites in other languages or architectures.

Developer and Assistant Training

From a novice to a senior, AI can help you improve your skills and understand the code better. Learning that new language or giving that trendy framework a chance will be easier if you have an assistant to guide you.

• Complete the code without memorizing the syntax
• Start with a pre-configured project
• Overcome the block of the first commit
• Rewrite your code in a new language

One of the most powerful features of AI is its continuous learning capability. Yes, as you use AI, it also learns and improves:

• Adapts its suggestions to the styles and preferences of the team.
• Evolves to adapt to changes in the project and in the technological stack.

This means that the more you use it, the better it will become in helping you write better-quality code.

🎁 Conclusion: Quality Matters and AI Can Help

AI in software development is not about generating code quickly but about creating better code. With the right tools and approach, it can be a valuable ally in your quest for excellence in programming. The next time someone tells you that AI only generates “ garbage at high speed”, you know there’s much more to the story.

The Future is Collaborative

AI is not here to replace developers but to enhance their capabilities. The key is finding the right balance between human creativity and AI efficiency. With the right tools and the AI-Driven Development approach, we can:

• Write cleaner and more maintainable code
• Reduce errors and vulnerabilities
• Increase productivity without sacrificing quality

Best Practices for Integrating AI into Development

To make the most of these tools, it’s important to follow some best practices:

• Ask clearly and specifically. Don’t settle for “suggest a solution,” and leave your needs and constraints clear.
• Add context. The more information you give the AI about the problem and the project environment, the better its suggestions will be.
• Configure and adapt the AI: Set the standards and needs specific to your project. Don’t use generic configurations.
• Integrate automatic tests: This gives you an extra layer of security.
• Always review the code: Don’t rely blindly on what the AI produces.

And how are you using AI in your development? The technology is evolving rapidly, and the possibilities are exciting. The important thing is to keep always the focus on quality and use these tools intelligently and responsibly.

Code smarter, not harder.

Originally published at https://aiddbot.com on November 24, 2024.