How does Atlas find duplicated Kotlin code if variable names are different?

Atlas uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, and indexes Kotlin code by AST declarations using tree-sitter. This allows it to understand the underlying logic and structure of your Kotlin code, identifying near-duplicate implementations even when variable names or minor syntax differ, which traditional `grep` commands cannot do.

Can Atlas create new Kotlin files and modules?

Yes, Atlas's `write` tool can create new Kotlin files and even entire modules. You can specify the file path, such as `src/main/kotlin/com/example/utils/MyHelper.kt`, and provide instructions for the content. Atlas will present a full diff for your approval before creating the file, ensuring you maintain control over your Kotlin codebase.

How does Atlas ensure my Kotlin refactoring is safe and doesn't break existing code?

Atlas ensures safety through several mechanisms. It drafts a plan in a read-only agent for your review, computes a unified diff for every file edit before writing, and uses `apply_patch` to create one independently reviewable and revertible patch per file. Crucially, the workflow mandates running `JUnit 5 via gradle test` after every single patch application, allowing immediate detection of regressions in your Kotlin project.

Does Atlas integrate with Gradle for Kotlin projects?

Absolutely. Atlas is designed to work direct with Kotlin projects using `Gradle`. It can read your `build.gradle.kts` configuration, understand your modules, and execute `Gradle` commands like `bash ./gradlew clean test` directly, making it a native part of your Kotlin development workflow.

Can Atlas help maintain Kotlin code style with `ktlint` during refactoring?

Yes, Atlas can assist with maintaining Kotlin code style. While Atlas itself doesn't automatically run `ktlint` on every edit, you can integrate `ktlint` into your workflow by running `bash ./gradlew ktlintFormat` or similar commands after Atlas applies patches, ensuring your refactored Kotlin code adheres to your project's formatting standards.

What if I need to roll back a change made by Atlas in my Kotlin project?

Atlas provides robust rollback capabilities. It snapshots file changes as git patches, allowing edits to be easily diffed and rolled back. Furthermore, because `apply_patch` creates one patch per file, each change is independently revertible, giving you fine-grained control over your Kotlin codebase's history.

Does Atlas send my Kotlin code to external servers for indexing?

No, Atlas prioritizes code privacy. It can build its code index with local Ollama embeddings, meaning your proprietary Kotlin code remains entirely on your local machine and is not sent to third-party servers for processing or indexing. This ensures your intellectual property is protected.

Extract a Shared Helper from Duplicated Kotlin Code with Atlas in 2026

To extract a shared helper from duplicated Kotlin code, Atlas in 2026 leverages its semantic search capabilities to identify near-identical logic across your project, even when variable names differ, then systematically refactors it into a single, tested utility. This process integrates directly with your existing Kotlin toolchain, including `Gradle` for dependency management, `JUnit 5 via gradle test` for verification, and `ktlint` for code formatting, ensuring a consistent and high-quality outcome.

How to find duplicated Kotlin code semantically with Atlas

Finding duplicated Kotlin code semantically is a core strength of Atlas in 2026, allowing developers to identify similar logic that traditional text-based `grep` commands would miss due to differing variable names. Atlas uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, to surface these near-duplicate implementations across your `src/main/kotlin/` directories.

Atlas excels at identifying duplicated logic in Kotlin codebases by understanding the underlying structure and meaning, rather than just exact text matches. This is crucial because copy-pasted code often undergoes minor modifications, such as renaming local variables or reordering statements, making it invisible to simple `grep` searches. Atlas builds its code index using AST declarations via tree-sitter, which provides a deep understanding of your Kotlin code's structure, including coroutines and Gradle configurations. You initiate this process by asking Atlas's `codebase_search` tool for the *behavior* you suspect is duplicated, not the exact code. For example, you might ask, "Find code that processes a list of items and returns a filtered result." Atlas can build its code index with local Ollama embeddings, ensuring your proprietary Kotlin code remains off third-party servers. The results from `codebase_search` will present potential duplicates, allowing you to review and confirm their equivalence before proceeding with refactoring.

Confirming Kotlin code duplication and creating a new helper module

After Atlas's `codebase_search` identifies potential duplicates in your Kotlin project, the next critical step in 2026 is to manually confirm that these code snippets are genuinely equivalent in their intent and behavior. Once confirmed, you can use Atlas's `write` tool to create a new shared helper module, complete with its own `src/main/kotlin/` file, ready for your extracted logic.

Once `codebase_search` provides a list of potential duplicate code blocks, you must carefully `read` each hit to confirm that the copies are genuinely equivalent and suitable for collapsing into a single helper. This human review step is vital to ensure the refactoring maintains correctness and does not introduce regressions. After confirming the duplication, you'll use Atlas's `write` tool to create the new shared Kotlin helper. For instance, you might instruct Atlas to `write a new Kotlin file at src/main/kotlin/com/example/project/utils/MySharedHelper.kt that contains a function named 'processItems'`. Atlas will draft a plan in a read-only plan agent and ask for your approval before switching to a build agent. Before the file is created, Atlas shows the full diff in the permission prompt, allowing you to review the proposed changes. This new file will serve as the central location for your extracted, tested logic, which will then be called from all the original duplicate locations. Atlas connects to Model Context Protocol servers and exposes their tools to the agent, ensuring a robust environment for code generation.

Replacing duplicated Kotlin code with helper calls and ensuring safety

Replacing each instance of duplicated Kotlin code with a call to your new shared helper is a systematic process in 2026, managed by Atlas's `apply_patch` tool, which ensures each modification is independently reviewable and revertible. This approach minimizes risk, allowing you to run your `JUnit 5 via gradle test` suite after every single swap to immediately catch any regressions.

With the shared Kotlin helper created, the next phase involves replacing each original duplicate code block with a call to the new helper function. Atlas facilitates this with its `apply_patch` tool. The key safety mechanism here is that Atlas generates one patch per file. For example, if you have duplicates in `src/main/kotlin/com/example/ServiceA.kt` and `src/main/kotlin/com/example/ServiceB.kt`, Atlas will create two separate patches. This granular approach means each swap is independently reviewable and revertible, providing a high degree of control and safety. After Atlas applies a patch to a file, you immediately run your test suite using the command `bash ./gradlew clean test`. This command executes all `JUnit 5` tests configured in your `build.gradle.kts` file, ensuring that the refactoring has not introduced any bugs. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, giving you full transparency. Atlas also reads git branches, status, and diffs, and can stage and create commits on your behalf, making version control integration direct.

Verifying refactoring completion and maintaining Kotlin code quality

After systematically replacing all identified duplicated Kotlin code with calls to your new shared helper and verifying each step with `JUnit 5 via gradle test`, the final verification in 2026 involves a comprehensive `grep` search to ensure no copies remain. Maintaining code quality is further supported by `ktlint`, which Atlas can integrate into its workflow for consistent formatting across your refactored codebase.

Once all duplicate instances have been replaced and verified by running `bash ./gradlew clean test` after each `apply_patch` operation, a final check is essential. You will use the `grep` command to search for any surviving copies of the original logic, ensuring a complete refactor. For example, `grep -r "original_logic_keyword" src/main/kotlin/` can confirm the absence of the old pattern. Atlas's ability to run `bash` commands directly makes this verification straightforward. Beyond functional correctness, maintaining code quality and consistency is paramount. Atlas can be configured to integrate with `ktlint`, your preferred Kotlin formatter. While Atlas performs edits, it can ensure that the newly created helper and the modified call sites adhere to your project's `ktlint` rules, preventing formatting drift. Atlas ships a TUI theme system with a charcoal-and-blue default theme and many presets, making the terminal experience pleasant during these detailed refactoring tasks. Atlas also snapshots file changes as git patches so edits can be diffed and rolled back, providing an additional layer of safety.

Step by step

01Ask Atlas's `codebase_search` for the behavior of the duplicated Kotlin logic, e.g., `atlas codebase_search "find Kotlin code that validates user input"`.
02Review each hit from `codebase_search` using `atlas read <file_path>` to confirm the Kotlin code copies are genuinely equivalent and suitable for extraction.
03Instruct Atlas's `write` tool to create the new shared Kotlin helper file, e.g., `atlas write a new Kotlin file at src/main/kotlin/com/example/utils/ValidationHelper.kt with a 'validateInput' function`.
04Use Atlas's `apply_patch` to replace each duplicate Kotlin code block with a call to the new helper, e.g., `atlas apply_patch "replace duplicated validation logic in src/main/kotlin/com/example/UserService.kt with a call to ValidationHelper.validateInput"`.
05After each `apply_patch` operation, run your `JUnit 5` tests using `bash ./gradlew clean test` to verify no regressions were introduced in your Kotlin project.
06Repeat the `apply_patch` and `bash ./gradlew clean test` steps for every identified duplicate Kotlin code block.
07Perform a final verification by running `bash grep -r "original_logic_keyword" src/main/kotlin/` to ensure no surviving copies of the old Kotlin logic remain.
08Optionally, use `ktlint` to format the newly created helper and modified files, ensuring consistent Kotlin code style across your project.

Frequently asked questions

How does Atlas find duplicated Kotlin code if variable names are different?: Atlas uses hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, and indexes Kotlin code by AST declarations using tree-sitter. This allows it to understand the underlying logic and structure of your Kotlin code, identifying near-duplicate implementations even when variable names or minor syntax differ, which traditional `grep` commands cannot do.
Can Atlas create new Kotlin files and modules?: Yes, Atlas's `write` tool can create new Kotlin files and even entire modules. You can specify the file path, such as `src/main/kotlin/com/example/utils/MyHelper.kt`, and provide instructions for the content. Atlas will present a full diff for your approval before creating the file, ensuring you maintain control over your Kotlin codebase.
How does Atlas ensure my Kotlin refactoring is safe and doesn't break existing code?: Atlas ensures safety through several mechanisms. It drafts a plan in a read-only agent for your review, computes a unified diff for every file edit before writing, and uses `apply_patch` to create one independently reviewable and revertible patch per file. Crucially, the workflow mandates running `JUnit 5 via gradle test` after every single patch application, allowing immediate detection of regressions in your Kotlin project.
Does Atlas integrate with Gradle for Kotlin projects?: Absolutely. Atlas is designed to work direct with Kotlin projects using `Gradle`. It can read your `build.gradle.kts` configuration, understand your modules, and execute `Gradle` commands like `bash ./gradlew clean test` directly, making it a native part of your Kotlin development workflow.
Can Atlas help maintain Kotlin code style with `ktlint` during refactoring?: Yes, Atlas can assist with maintaining Kotlin code style. While Atlas itself doesn't automatically run `ktlint` on every edit, you can integrate `ktlint` into your workflow by running `bash ./gradlew ktlintFormat` or similar commands after Atlas applies patches, ensuring your refactored Kotlin code adheres to your project's formatting standards.
What if I need to roll back a change made by Atlas in my Kotlin project?: Atlas provides robust rollback capabilities. It snapshots file changes as git patches, allowing edits to be easily diffed and rolled back. Furthermore, because `apply_patch` creates one patch per file, each change is independently revertible, giving you fine-grained control over your Kotlin codebase's history.
Does Atlas send my Kotlin code to external servers for indexing?: No, Atlas prioritizes code privacy. It can build its code index with local Ollama embeddings, meaning your proprietary Kotlin code remains entirely on your local machine and is not sent to third-party servers for processing or indexing. This ensures your intellectual property is protected.

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