AI Tools for Kotlin Development: 2024 Overview

AI Tools for Kotlin Development: 2024 Overview

Kotlin development continues to gain momentum, especially in the field of Android applications and server-side solutions. However, modern developers can no longer rely solely on their experience — artificial intelligence tools come to the rescue. In this article, we will review the best AI solutions that significantly speed up writing code, refactoring, and debugging Kotlin projects.

Why is AI Important for a Kotlin Developer?

Kotlin is a concise and expressive language, but its strong typing and coroutine features require attention. AI tools help:

• Reduce time spent on writing boilerplate code — AI generates typical blocks (data classes, sealed classes, mappers).
• Improve code quality — tools suggest optimal patterns and avoid common mistakes.
• Automate testing — generation of unit tests for coroutines and Flow.
• Speed up migration from Java — AI helps convert Java code to Kotlin, taking into account language idioms.

Top Tools for Working with Kotlin

• GitHub Copilot — one of the most popular AI assistants. It understands the context of Kotlin projects well, offering autocompletions for coroutines, Jetpack Compose, and Ktor. Example: when writing viewModelScope.launch, Copilot immediately suggests proper exception handling.
• Tabnine — an alternative to Copilot that works completely locally. Supports Kotlin and KMP (Kotlin Multiplatform). Feature: analyzes the entire project for more accurate predictions.
• JetBrains AI Assistant — native integration into IntelliJ IDEA and Android Studio. It can not only complete code but also explain complex constructs, refactor legacy code, and write documentation in KDoc format.
• Codeium — a free alternative to Copilot with Kotlin support. Useful for writing tests and generating boilerplate code. It also works in VS Code if you write Kotlin for the backend.
• Sourcegraph Cody — AI for working with large codebases. Helps understand someone else's Kotlin code, find dependencies, and bugs in coroutines.

Practical Use Cases

Let's look at how these tools help in real-world tasks:

• Writing Compose components. AI assistants generate preview functions, state hoisting, and modifiers, saving hours of manual work.
• Refactoring coroutines. Tools suggest replacing runBlocking with viewModelScope or properly structuring flow.
• Working with KMP. AI helps write expect/actual declarations, taking into account platform specifics (iOS, Android, JVM).
• Documentation generation. Instead of manually writing KDoc, JetBrains AI Assistant creates descriptions of parameters and return values.

Tips for Implementation

To get the most out of AI tools, follow these simple rules:

• Don't blindly accept AI suggestions — always check the logic, especially in multithreaded