Ruchy Analyze: Binary Analysis & Optimization Tool Request

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Ruchy Analyze: A Feature Request for Binary Analysis and Optimization Tooling

Hey guys! Today, we're diving deep into a feature request that could seriously level up the Ruchy development experience: a binary analysis and optimization tool, which we’re calling ruchy analyze. This isn't just some nice-to-have; it's a game-changer for understanding and optimizing our compiled Ruchy programs. So, let's break down what this is all about, why it matters, and how it can make our lives as developers a whole lot easier.

The Motivation Behind ruchy analyze

Problem: The Mystery of Binary Size and Optimization

Let's face it, one of the biggest challenges in software development is understanding the inner workings of our compiled binaries. As Ruchy developers, we often find ourselves in the dark about several critical aspects:

  • Binary Size Breakdown: Which sections of our compiled code are consuming the most space? Is it the text, the data, or something else entirely?
  • Optimization Opportunities: Where can we trim the fat? Are there chunks of dead code lurking in the shadows? Are there functions ripe for inlining?
  • Startup Performance Overhead: What’s causing delays during application startup? Is it the loader, the linker, or something else?
  • Relocation Costs: How much overhead are we incurring from relocations?

Without clear insights into these areas, we're essentially flying blind. We can't systematically optimize our binary size or effectively identify those pesky performance bottlenecks. And that's where the ruchy analyze tool comes into the picture.

Impact: Why This Matters

The inability to see inside our binaries has some serious consequences. It means:

  • Bloated Binaries: Our programs can end up larger than necessary, which impacts storage, distribution, and even runtime performance.
  • Performance Bottlenecks: Startup times can suffer, and applications can feel sluggish due to inefficient code.
  • Wasted Resources: We spend valuable time guessing and experimenting instead of making data-driven optimization decisions.

The impact is clear: we need a tool that sheds light on the inner workings of our binaries. This isn't just about making our programs smaller; it's about making them faster, more efficient, and easier to maintain. By having better visibility, we can write better code and deliver better applications. This, in turn, enhances the entire Ruchy ecosystem, making it more competitive and attractive to developers.

Proposed Solution: ruchy analyze to the Rescue

Our proposed solution is to introduce a new subcommand: ruchy analyze. This command will provide a comprehensive binary analysis, giving us the insights we need to optimize our code. Think of it as a powerful microscope for our binaries, allowing us to see exactly what’s going on under the hood. This tool will not only help us reduce binary size but also improve the overall performance of our Ruchy applications. It's a win-win!

Diving Deep into the Proposed Feature

Command Syntax: Simple and Straightforward

First things first, let's talk about how we'll actually use this tool. The command syntax is designed to be intuitive and easy to remember:

ruchy analyze [OPTIONS] <binary-file>

Pretty straightforward, right? You simply call ruchy analyze, specify any options you need, and then provide the path to the binary file you want to analyze. It's clean, it's simple, and it gets the job done.

Analysis Modes: A Toolkit for Every Need

Now, let's get to the juicy part: the analysis modes. ruchy analyze isn't just a one-trick pony; it comes packed with a suite of features to help us dissect our binaries from every angle. Here’s a breakdown of the key modes:

1. Binary Size Analysis: Know Where the Bytes Are

This mode is all about understanding how our binary is structured and where the space is being used. With a simple command:

ruchy analyze --size --output=size.json compiled_binary

We can generate a size.json file that gives us a detailed breakdown of the binary's sections. The output looks something like this:

{
  "sections": {
    "text": {"size": 770560, "percentage": 62.3},
    "rodata": {"size": 111520, "percentage": 9.0},
    "data": {"size": 2552, "percentage": 0.2},
    "bss": {"size": 8192, "percentage": 0.7}
  },
  "total_size": 1236480,
  "format": "ELF"
}

This JSON output tells us exactly how much space each section (like .text, .rodata, .data, and .bss) is consuming, both in absolute size and as a percentage of the total. This is crucial for identifying areas where we can potentially reduce the binary footprint. Understanding where our binary’s size is coming from is the first step toward making it leaner and meaner.

2. Symbol Table Analysis: Uncover the Functions and Their Sizes

The symbol table is a treasure trove of information about the functions and variables in our code. This analysis mode lets us tap into that treasure. By running:

ruchy analyze --symbols --output=symbols.json compiled_binary

We can generate a symbols.json file that contains a wealth of insights. Here’s a sample of what the output might look like:

{
  "symbols": [
    {"name": "fibonacci", "address": "0x1234", "size": 128, "type": "function"},
    {"name": "main", "address": "0x1300", "size": 64, "type": "function"}
  ],
  "inlining_candidates": [
    {"name": "main", "size": 64, "reason": "small function (<64 bytes)"}
  ],
  "total_functions": 42,
  "average_function_size": 156
}

This output gives us a list of symbols (functions and variables), their addresses, sizes, and types. But it doesn't stop there! It also identifies potential inlining candidates – functions that are small enough to be inlined, which can lead to performance improvements. Knowing which functions are taking up the most space and which ones are good candidates for inlining is key to optimizing our code. This detailed analysis can guide us in making strategic decisions about how to structure and optimize our functions for better performance and smaller size.

3. Startup Time Profiling: Optimize for Speed

Nobody likes waiting for an application to start up. This mode helps us diagnose and fix those slow startup times. By running:

ruchy analyze --startup --output=startup.json compiled_binary

We can get a breakdown of the startup process in the startup.json file:

{
  "startup_time_us": 450,
  "loader_time_us": 120,
  "linking_time_us": 230,
  "init_time_us": 100,
  "breakdown": {
    "loader": 26.7,
    "linking": 51.1,
    "init": 22.2
  }
}

This output shows the total startup time, as well as the time spent in different phases like loading, linking, and initialization. The breakdown gives us a percentage view, allowing us to quickly identify the biggest culprits. Armed with this information, we can focus our optimization efforts on the areas that will yield the most significant improvements. For example, if linking time is high, we might consider reducing dependencies or optimizing linking settings.

4. Relocation Analysis: Reduce Overhead

Relocations are a necessary part of the linking process, but they can also introduce overhead. This mode helps us understand and minimize that overhead. By running:

ruchy analyze --relocations --output=reloc.json compiled_binary

We can generate a reloc.json file with details about relocations:

{
  "total_relocations": 234,
  "relocation_types": {
    "R_X86_64_RELATIVE": 180,
    "R_X86_64_GLOB_DAT": 42,
    "R_X86_64_JUMP_SLOT": 12
  },
  "overhead_bytes": 1872
}

This output shows the total number of relocations, a breakdown by relocation type, and the total overhead in bytes. By understanding the types of relocations that are most prevalent, we can take steps to reduce them, such as using position-independent code (PIC) or optimizing our data layout. Reducing relocation overhead can lead to faster load times and improved runtime performance.

5. Optimization Recommendations: Let the Tool Guide You

This is where ruchy analyze really shines. It doesn't just give us data; it gives us actionable advice. By running:

ruchy analyze --optimize --output=optim.json compiled_binary

We can get a set of optimization recommendations in the optim.json file:

{
  "recommendations": [
    {
      "type": "dead_code_elimination",
      "description": "Remove unused function: unused_helper",
      "location": "main.ruchy:42",
      "impact_bytes": 256,
      "priority": "high",
      "confidence": 0.95
    },
    {
      "type": "function_inlining",
      "description": "Inline small function: get_value",
      "location": "utils.ruchy:15",
      "impact_bytes": 64,
      "priority": "medium",
      "confidence": 0.85
    },
    {
      "type": "function_outlining",
      "description": "Outline cold error handling path",
      "location": "error.ruchy:88",
      "impact_bytes": 128,
      "priority": "medium",
      "confidence": 0.75
    }
  ],
  "total_potential_savings_bytes": 448,
  "total_potential_savings_percent": 3.7
}

These recommendations cover a range of optimization techniques, such as dead code elimination, function inlining, and function outlining. Each recommendation includes a description, location, estimated impact, priority, and confidence level. This makes it easy to prioritize our optimization efforts and focus on the areas where we can achieve the biggest gains. The tool even estimates the potential savings in bytes and as a percentage of the total binary size, giving us a clear picture of the potential impact of each optimization.

6. Format Detection: Know Your Binary

Sometimes, we just need to know the basics about our binary: what format is it, what architecture is it built for, and so on. This mode gives us that information. By running:

ruchy analyze --format --output=format.json compiled_binary

We can get a format.json file with the essential details:

{
  "format": "ELF",
  "architecture": "x86-64",
  "bits": 64,
  "endianness": "little",
  "entry_point": "0x1060",
  "sections": 28,
  "segments": 13
}

This output provides details like the binary format (ELF, Mach-O, PE), architecture (x86-64, ARM), bitness (32-bit, 64-bit), endianness, entry point, and the number of sections and segments. This is crucial for ensuring that our binaries are built correctly and compatible with the target platform. It also provides a solid foundation for more in-depth analysis.

Implementation Details: How It All Works

Dependencies: Standing on the Shoulders of Giants

To build this powerful tool, we'll be relying on some fantastic open-source libraries. Here are the key dependencies:

  • goblin (v0.8+): This is our workhorse for multi-platform binary parsing. It supports ELF, Mach-O, and PE formats, making ruchy analyze truly cross-platform.
  • serde_json: This library will handle the JSON output formatting, ensuring that our reports are clean, readable, and easy to integrate into other tools.

By leveraging these well-established libraries, we can focus on the Ruchy-specific aspects of the analysis and optimization, rather than reinventing the wheel. These dependencies provide a solid foundation, allowing us to build a robust and reliable tool.

Architecture: A Peek Under the Hood

Let's take a quick look at the architecture of the ruchy analyze tool. At its core, we'll have a BinaryAnalyzer struct that encapsulates the analysis logic:

// src/compiler/analyze.rs

pub struct BinaryAnalyzer {
    binary_path: PathBuf,
    format: BinaryFormat,
}

impl BinaryAnalyzer {
    pub fn analyze_size(&self) -> Result<SizeAnalysis>;
    pub fn analyze_symbols(&self) -> Result<SymbolAnalysis>;
    pub fn analyze_startup(&self) -> Result<StartupAnalysis>;
    pub fn analyze_relocations(&self) -> Result<RelocationAnalysis>;
    pub fn generate_recommendations(&self) -> Result<Vec<Recommendation>>;
    pub fn detect_format(&self) -> Result<FormatInfo>;
}

The BinaryAnalyzer will take the path to the binary and its format as input. It will then provide methods for performing each of the analysis modes we discussed earlier: size analysis, symbol analysis, startup time profiling, relocation analysis, optimization recommendations, and format detection. Each method will return a Result, allowing us to handle errors gracefully. This architecture is designed to be modular and extensible, making it easy to add new analysis modes and features in the future.

Platform Support: Cross-Platform from Day One

One of the key goals for ruchy analyze is to be cross-platform. We want developers on Linux, macOS, and Windows to be able to use the tool without any hassle. Here's the planned platform support:

Platform Format Status
Linux ELF ✅ Supported
macOS Mach-O ✅ Supported
Windows PE ✅ Supported

Thanks to the goblin crate, we can achieve this cross-platform support with relative ease. This means that no matter what operating system you're using, you'll be able to take advantage of the powerful analysis capabilities of ruchy analyze.

Use Cases: Real-World Applications

So, how will developers actually use ruchy analyze in their day-to-day work? Let's walk through a couple of key use cases.

1. Binary Size Optimization: Making Our Programs Lean

Imagine you've compiled your Ruchy program and you're a bit concerned about its size. Here's how you can use ruchy analyze to optimize it:

# Compile program
ruchy compile main.ruchy --output app

# Analyze size breakdown
ruchy analyze --size --output=size.json app

# Get optimization recommendations
ruchy analyze --optimize --output=optim.json app

# Apply recommendations and recompile
ruchy compile main.ruchy --output app --optimize=size

# Verify improvement
ruchy analyze --size app

In this workflow, we first compile our program. Then, we use ruchy analyze to get a size breakdown and optimization recommendations. We apply those recommendations (either manually or by using compiler flags like --optimize=size), recompile, and then verify the improvement with another size analysis. The expected outcome is a significant reduction in binary size, often in the range of 30-50%. This is achieved through techniques like:

  • Dead Code Elimination: Removing unused functions and variables.
  • Function Inlining: Replacing function calls with the function body to reduce overhead.
  • Outlining Cold Paths: Moving rarely executed code (like error handling) to separate functions.
  • Symbol Stripping: Removing unnecessary symbols from the binary.

By systematically applying these optimizations, we can make our programs smaller, faster, and more efficient.

2. Performance Profiling: Speeding Things Up

Startup time is critical for user experience. If your application takes too long to start, users may get frustrated and abandon it. Here's how ruchy analyze can help:

# Analyze startup overhead
ruchy analyze --startup app

# Identify slow initialization
# Optimize by lazy-loading or reducing dependencies

# Re-analyze to verify improvement
ruchy analyze --startup app_optimized

In this scenario, we use ruchy analyze to profile the startup time of our application. We identify any slow initialization steps and then optimize them, for example, by lazy-loading resources or reducing dependencies. Finally, we re-analyze the startup time to verify that our optimizations have had the desired effect. This iterative process allows us to systematically improve the startup performance of our applications, leading to a better user experience.

3. CI/CD Integration: Catch Regressions Early

ruchy analyze isn't just for local development; it can also be integrated into our Continuous Integration and Continuous Deployment (CI/CD) pipelines. For example, we can use GitHub Actions to check for binary size regressions automatically:

- name: Check binary size regression
  run: |
    ruchy analyze --size --output=size.json app
    ./scripts/check_size_regression.sh size.json

In this example, we run ruchy analyze as part of our CI pipeline. We generate a size report and then use a script to check if the binary size has increased compared to a previous build. If a regression is detected, the CI pipeline will fail, alerting us to the issue before it makes its way into production. This proactive approach helps us maintain the quality and performance of our applications over time.

Validation: Proof of Concept

To demonstrate the feasibility of this feature, a prototype of ruchy analyze has already been developed. Let's take a look at its status and performance.

Prototype Status: Already in Action

A working prototype of ruchy analyze has been implemented and is available on GitHub:

  • Repository: https://github.com/paiml/ruchyruchy
  • Branch: main
  • Tests: 6/6 passing (100%)
  • Implementation: ~890 LOC (490 LOC tests + 400 LOC implementation)
  • Test file: tests/test_compiled_inst_003_binary_analysis.rs

The prototype includes implementations for all the core analysis modes: binary size breakdown, symbol table analysis, startup time profiling, relocation overhead analysis, optimization recommendations, and ELF format support. The fact that all tests are passing demonstrates the robustness and reliability of the implementation. With approximately 890 lines of code, the prototype is a significant proof of concept, showing that this feature is not just a pipe dream but a practical reality.

Test Results: Solid Performance

The prototype has been thoroughly tested, and the results are encouraging. Here's a summary of the test results:

running 6 tests
test test_binary_size_breakdown ... ok
test test_symbol_table_analysis ... ok
test test_startup_time_profiling ... ok
test test_relocation_overhead ... ok
test test_optimization_recommendations ... ok
test test_elf_format_support ... ok

test result: ok. 6 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

As you can see, all six tests passed successfully, covering the key analysis modes of ruchy analyze. This gives us confidence that the tool is functioning correctly and providing accurate results. The comprehensive test suite ensures that the tool is reliable and can be trusted to provide valuable insights into our binaries.

Performance Validation: Fast and Efficient

Performance is a critical factor for any development tool. We don't want ruchy analyze to become a bottleneck in our workflow. The prototype has been measured on a 1.2MB release build of the ruchyruchy binary, and the results are impressive:

  • Binary size analysis: <1ms
  • Symbol extraction: <5ms
  • Format detection: <0.1ms
  • Zero runtime overhead (static analysis only)

These measurements show that ruchy analyze is incredibly fast and efficient. The analysis times are so low that they won't add any noticeable overhead to our development process. The fact that it's a static analysis tool means that it doesn't introduce any runtime overhead, so we can use it without worrying about impacting the performance of our applications. This makes ruchy analyze a practical and valuable tool for everyday use.

Compiler Optimizations Verified: Making the Most of Ruchy

To ensure that ruchy analyze is working effectively, we've also verified that it correctly identifies and benefits from compiler optimizations. We've tested the tool with various optimization levels and settings, including:

  • opt-level = 3: Maximum optimization ✅
  • LTO = true: Link-time optimization ✅
  • codegen-units = 1: Single unit optimization ✅
  • Result: 98% size reduction (39M debug → 1.2M release)

These tests demonstrate that ruchy analyze can help us take full advantage of Ruchy's compiler optimizations. By using the tool in conjunction with these optimizations, we can achieve significant reductions in binary size and improvements in performance. The 98% size reduction achieved by combining compiler optimizations with ruchy analyze is a testament to the power of this approach.

Documentation: Ready to Use

Comprehensive documentation is essential for any tool, and ruchy analyze is no exception. A complete book chapter has been written, documenting the tool's features, usage, and implementation details. The documentation follows the RED-GREEN-REFACTOR Test-Driven Development (TDD) cycle, ensuring that it's accurate, up-to-date, and easy to follow. This comprehensive documentation makes it easy for developers to learn how to use ruchy analyze and integrate it into their workflows.

Benefits: Why This Matters

The benefits of ruchy analyze are wide-ranging and impactful. Let's break them down for developers, the Ruchy project, and the broader ecosystem.

For Developers: Empowering Optimization

For us developers, ruchy analyze offers a wealth of benefits:

  1. Visibility: We gain a clear understanding of what's inside our binaries, eliminating the guesswork and allowing us to make informed decisions.
  2. Optimization: We get actionable recommendations with impact estimates, making it easy to prioritize our optimization efforts and achieve the biggest gains.
  3. Debugging: We can identify bloat and unused code, helping us keep our programs lean and efficient.
  4. Performance: We can optimize startup time and relocations, leading to faster and more responsive applications.

By empowering us with these capabilities, ruchy analyze makes us more effective and efficient developers. We can spend less time guessing and experimenting, and more time writing high-quality code.

For the Ruchy Project: Building a Competitive Edge

ruchy analyze also offers significant benefits for the Ruchy project as a whole:

  1. Competitive: It allows us to match or even beat C binary sizes, making Ruchy a more attractive option for performance-sensitive applications.
  2. Quality: It enables a systematic optimization workflow, ensuring that our programs are consistently lean and efficient.
  3. Education: It teaches developers about binary structure, helping them become more knowledgeable and skilled.
  4. Debugging: It helps users debug size and performance issues, making the Ruchy ecosystem more robust and user-friendly.

By providing these benefits, ruchy analyze helps us build a stronger and more competitive Ruchy ecosystem.

For the Ecosystem: Fostering Innovation

Finally, ruchy analyze benefits the broader ecosystem by:

  1. Tooling: Providing rich analysis capabilities that can be integrated into IDEs and other development tools.
  2. CI/CD: Enabling automated size regression detection, helping us maintain the quality of our applications over time.
  3. Benchmarking: Providing a tool for comparing Ruchy's performance with other languages, driving innovation and improvement.
  4. Research: Providing data for compiler optimization research, helping us push the boundaries of what's possible.

By fostering innovation and collaboration, ruchy analyze helps us build a vibrant and thriving ecosystem around Ruchy.

Alternatives Considered: Why ruchy analyze Is the Right Choice

Before proposing ruchy analyze, we carefully considered several alternative approaches. Let's take a look at why we believe ruchy analyze is the best solution.

1. Use External Tools: A Fragmented Approach

One option would be to rely on existing external tools like objdump, nm, and readelf. While these tools provide valuable information, they have several drawbacks:

  • Requires users to learn multiple tools
  • No Ruchy-specific optimization recommendations
  • No integration with the compiler
  • Platform-specific (not cross-platform)

By providing a unified and Ruchy-aware tool, we can offer a much better developer experience.

2. Runtime Profiling Only: Missing the Full Picture

Another option would be to focus solely on runtime profiling, without static analysis. However, this approach has limitations:

  • Misses dead code detection
  • Requires test coverage for all paths
  • Higher overhead
  • Cannot analyze unexecuted code

By combining static and runtime analysis, we can get a more complete picture of our program's behavior.

3. Minimal Size Reporting: Too Coarse-Grained

A third option would be to simply report the total binary size, without providing any detailed analysis. While this would be better than nothing, it's not sufficient for effective optimization:

  • Too coarse-grained for optimization
  • No actionable recommendations
  • Cannot identify bottlenecks

By providing detailed analysis and actionable recommendations, ruchy analyze empowers us to make informed optimization decisions.

Our selected approach: Comprehensive static analysis with actionable recommendations. This approach provides the best balance of power, flexibility, and ease of use.

Success Criteria: Measuring Our Progress

To ensure that ruchy analyze is a success, we'll be tracking several key metrics. Let's define our success criteria for the Minimum Viable Product (MVP), V1.0, and future enhancements.

Minimum Viable Product (MVP): The Essentials

Our MVP will include the following features:

  • ✅ Binary size breakdown (text, data, rodata, bss)
  • ✅ Symbol table extraction
  • ✅ Format detection (ELF, Mach-O, PE)
  • ✅ JSON export for CI integration
  • ✅ <10ms analysis time for typical binaries

These features provide the core analysis capabilities that developers need to start optimizing their programs.

V1.0 Goals: Expanding Our Horizons

V1.0 will build on the MVP by adding:

  • ✅ All MVP features
  • ✅ Optimization recommendations
  • ✅ Startup time profiling
  • ✅ Relocation analysis
  • ⏳ Integration with ruchy compile --optimize flags
  • ⏳ HTML report generation
  • ⏳ Flame graph support

These features will significantly enhance the usability and power of ruchy analyze.

Future Enhancements: The Road Ahead

Looking ahead, we envision several future enhancements, including:

  • Machine learning for recommendation prioritization
  • Comparative analysis (vs C/Rust/Go binaries)
  • Cache profiling integration
  • Symbol deduplication detection
  • Compression analysis

These enhancements will further solidify ruchy analyze as a cutting-edge binary analysis and optimization tool.

Timeline: Mapping Out the Journey

To ensure that ruchy analyze is delivered in a timely manner, we've developed a detailed timeline. Here's a breakdown of the key phases:

Phase 1: Core Infrastructure (Week 1-2)

  • Integrate goblin crate
  • Implement basic binary parsing
  • Add ruchy analyze --size command

Phase 2: Symbol Analysis (Week 3-4)

  • Symbol table extraction
  • Inlining candidate detection
  • Add --symbols flag

Phase 3: Optimization Recommendations (Week 5-6)

  • Dead code detection
  • Function outlining suggestions
  • Impact estimation
  • Add --optimize flag

Phase 4: Integration & Documentation (Week 7-8)

  • CI/CD examples
  • VSCode extension integration
  • Comprehensive documentation
  • Tutorial videos

Total timeline: 8 weeks for production-ready implementation

References: Learning from the Best

To ensure that ruchy analyze is built on solid foundations, we've consulted a wide range of resources and prior art. Let's take a look at some key references.

Research Foundation: The Building Blocks

Our research foundation includes resources on:

  1. Binary Analysis:
    • goblin crate: Cross-platform binary parsing
    • ELF specification (System V ABI)
    • Mach-O format (Apple docs)
    • PE format (Microsoft docs)
  2. Optimization:
    • Dead code elimination (DCE) algorithms
    • Function inlining heuristics (<64 bytes)
    • Profile-guided optimization (PGO) research
  3. Performance:
    • Startup time profiling techniques
    • Relocation overhead measurement
    • Binary size optimization strategies

Prior Art: Learning from Experience

We've also studied existing tools and techniques, including:

  • Bloaty McBloatface (Google): Binary size profiler
  • cargo-bloat (Rust): Cargo plugin for binary size analysis
  • size (GNU binutils): Section size reporting
  • objdump, nm, readelf: Binary inspection tools

By learning from these resources and tools, we can ensure that ruchy analyze is a state-of-the-art binary analysis and optimization tool.

Documentation: Guiding the Way

Comprehensive documentation is essential for any tool, and ruchy analyze is no exception. We plan to provide both a user guide and API documentation.

User Guide: A Practical Handbook

The user guide will provide practical information on how to use ruchy analyze, including:

# ruchy analyze - Binary Analysis Tool

## Usage

ruchy analyze [OPTIONS] <binary-file>

## Options

--size              Analyze binary size breakdown
--symbols           Extract symbol table
--startup           Profile startup time
--relocations       Analyze relocation overhead
--optimize          Generate optimization recommendations
--format            Detect binary format
--output=<file>     Export JSON report

## Examples

# Quick size check
ruchy analyze --size app

# Full analysis
ruchy analyze --size --symbols --optimize --output=report.json app

# CI integration
ruchy analyze --size --output=size.json app || exit 1

API Documentation: A Developer's Reference

The API documentation will provide detailed information on the ruchy analyze API, including:

/// Analyze compiled binary structure
pub fn analyze_binary(
    binary_path: &Path,
    options: AnalyzeOptions
) -> Result<AnalysisReport>

/// Binary analysis options
pub struct AnalyzeOptions {
    pub size: bool,
    pub symbols: bool,
    pub startup: bool,
    pub relocations: bool,
    pub optimize: bool,
    pub format: bool,
}

/// Complete analysis report
pub struct AnalysisReport {
    pub size: Option<SizeAnalysis>,
    pub symbols: Option<SymbolAnalysis>,
    pub startup: Option<StartupAnalysis>,
    pub relocations: Option<RelocationAnalysis>,
    pub recommendations: Option<Vec<Recommendation>>,
    pub format: Option<FormatInfo>,
}

With comprehensive documentation, developers will be able to quickly learn how to use ruchy analyze and integrate it into their workflows.

Conclusion: Let's Make It Happen

The ruchy analyze command is an essential tool for binary optimization and performance analysis. With a proven prototype, comprehensive documentation, and clear use cases, this feature is ready for production integration. This tool will significantly improve the development experience and help us create faster, leaner, and more efficient applications.

Request: We kindly request that you review this feature request and consider integrating ruchy analyze into the production paiml/ruchy compiler. This will be a game-changer for the Ruchy community, enabling us to create even better software.

Contact: I am available for collaboration on implementation and testing. Let’s work together to make this happen!

Prototype: You can explore the prototype at https://github.com/paiml/ruchyruchy (COMPILED-INST-003). Feel free to dive in and see the power of ruchy analyze for yourself.

This is an exciting opportunity to elevate the Ruchy development experience and set a new standard for binary analysis and optimization. Let's make it happen!