Devin

Executes large-scale code refactoring tasks (e.g., data class migrations, architectural rewrites) by decomposing them into subtasks, analyzing code structure via AST or semantic understanding, and applying transformations across multiple files while maintaining import consistency. Operates in a human-in-the-loop model where each refactoring batch requires explicit human approval before commit, preventing autonomous drift while enabling high-velocity execution on repetitive structural changes.

Analyzes and updates import statements and dependency references across multiple files during refactoring by building a semantic model of the codebase's import graph. Traces transitive dependencies, identifies unused imports, and updates references when code is moved or restructured, ensuring consistency across the entire codebase without manual import management.

Breaks down large refactoring tasks into independent subtasks that can be executed in parallel by multiple Devin instances, coordinating results and merging outputs. Identifies task boundaries (e.g., refactoring data classes in different modules independently) and distributes work to reduce total execution time while maintaining consistency across subtask outputs.

Handles variations and edge cases in code structure during refactoring by learning from examples or specifications provided during setup. Applies transformations that account for non-standard patterns, legacy code, or domain-specific conventions rather than applying rigid, rule-based transformations. Uses fine-tuning or in-context learning to adapt to codebase-specific patterns.

Implements a human-in-the-loop approval workflow where refactored code changes are presented to human reviewers for explicit approval before being merged or deployed. Provides change summaries, diffs, and context to enable informed review decisions. Prevents autonomous code deployment while maintaining high-velocity execution on approved changes.

Executes refactoring tasks on massive codebases (6M+ lines of code, 100K+ files) by managing memory, context, and execution complexity at scale. Handles large-scale transformations that would be impractical for manual teams or traditional tooling by distributing work and maintaining consistency across the entire codebase.

Learns how to approach refactoring subtasks by analyzing examples or specifications provided during setup, enabling adaptation to codebase-specific patterns without explicit rule-based configuration. Uses fine-tuning or in-context learning to internalize task-specific knowledge and apply it consistently across the refactoring job.

Manages refactoring projects by tracking progress, organizing subtasks, and maintaining visibility into Devin's work. Provides project-level oversight and change tracking to enable human managers to monitor progress, approve batches of changes, and coordinate with engineering teams. Integrates with version control systems for change logging and audit trails.

Optimizes cost-per-task by automating high-volume, repetitive refactoring work that would otherwise require expensive engineer-hours. Achieves 20x cost savings (per Nubank case) by replacing manual refactoring with autonomous execution, reducing total cost of ownership for large-scale migrations while maintaining code quality through human approval.