depyler

The Python Standard Library Testing Roadmap tracks the validation and testing of Python 3.12 standard library modules for Depyler transpilation. The goal is to achieve 100% coverage of the ~200+ modules in the Python 3.12 standard library.

Modules are prioritized based on:

1. Usage Frequency: How common they are in real-world code.
2. Transpilation Complexity: Rated as LOW, MEDIUM, HIGH, or EPIC.
3. Dependencies: Prerequisites required for other modules.
4. Value: The capability unlocked for users upon successful transpilation.

Depyler is a Python-to-Rust transpiler designed for energy-efficient, safe code generation. It converts a practical subset of Python into idiomatic, high-performance Rust code while providing formal correctness guarantees through progressive verification.

Key benefits include:

• Energy Efficiency: Optimized Rust output reduces runtime energy consumption.
• Type Safety: Leverages Rust's type system to enforce safety on Python code.
• Zero-Runtime Overhead: Safety is verified at compile-time.
• Formal Verification: Uses property-based testing and contract systems to ensure semantic equivalence between Python and Rust.

Depyler provides significant performance, memory, and energy efficiency improvements by transpiling Python code to Rust. Benchmarks demonstrate that compared to a Python baseline, Depyler-generated Rust code is typically:

• 15.3x faster in execution time.
• 6.2x more memory efficient (84% reduction).
• 11.8x more energy efficient (91% reduction).
• 12.7x higher in throughput.

These improvements are observed across mathematical workloads, data processing, web servers, and scientific computing.

Depyler's ability to transpile Python to Rust is measured by its coverage of Python's standard library and core language features. As of October 2025, the current pass rate is approximately 71.3% (72/101 tests).

Key Coverage Areas:

• Complete (100%): Built-in types (literals, operators), control flow (if/else, loops), basic functions, collections (lists, dicts, sets, strings), basic classes (init, methods, properties), and asyncio (async/await).
• Partial: Context managers (60%), Type annotations (60%), and Iterators (60%).
• Not Implemented (0%): Exception handling, Generators, Pattern Matching (match/case), and Advanced features like Lambdas/Closures.

Use this analysis to understand the limitations of the current transpiler when processing real-world Python codebases.

The documentation for Depyler is organized into modules. For the test_docs_minimal module, you can access the following resources:

• Module Documentation: High-level overview of the module.
• API Reference: Detailed technical specifications of the public API.
• Usage Guide: Practical instructions and examples for using the module.

Depyler (Rust-based) provides significant performance improvements over Python and NumPy across various domains. Key performance characteristics include:

• Scientific Computing: Up to 8.5x faster simulation time and 2.6x less peak memory usage compared to Python + NumPy in molecular dynamics workloads.
• Fluid Dynamics: Up to 8.5x faster execution and significantly lower energy consumption compared to NumPy/SciPy.
• Edge/Mobile: Up to 7.5x less energy consumption and significantly lower operating temperatures on devices like Raspberry Pi 4.
• IoT/Battery: Up to 6.8x longer battery life on ESP32 and 7.4x longer on Arduino-class devices.
• General Gains: Significant improvements in execution speed (up to 35.3x), memory efficiency (up to 13.0x less), and energy reduction (up to 28.8x less).

Renacer is a pure Rust system call tracer used to discover hidden transpilation patterns and debug issues in Depyler (specifically targeting bugs like DEPYLER-0456 and DEPYLER-0457). It provides several key capabilities for analyzing the transpilation process:

• Transpiler Source Mapping: Maps Rust compilation errors back to the original Python source code.
• Decision Trace: Captures specific compile-time decisions made by the transpiler.
• Function Profiling: Identifies hot paths and I/O bottlenecks during transpilation.
• Syscall Tracing: Reveals file access patterns occurring during the transpilation process.
• OTLP Export: Exports transpilation telemetry to Jaeger for visual analysis.

Depyler follows a strictly sequential development plan where each priority level must be 100% complete before moving to the next. Each completed priority triggers a new minor version release (e.g., v1.1.0, v1.2.0).

Versioning Scheme:

• v1.0.x: Bug fixes only.
• v1.x.0: Released upon completion of each priority level.
• v2.0.0: Major release for production readiness, including optimizations and tooling.

Development Principles:

• Sequential Execution: No skipping priorities.
• Quality Gates: Every priority must pass all quality checks (tests, documentation, benchmarks, etc.) before release.
• Incremental Releases: Each priority completion results in a new minor version.