Use Aider bencmarks in Roo to see they compare against each other

[KJ7LNW]

AI Model Benchmarking System for Roo

Claude was guided to create this based on my input, forgive any AI slop, but I thought I would propose this if anyone is interested in taking on the project because it really could help Roo grow and perform even better than it does now.

-Eric

Introduction

The proposed AI Model Benchmarking System for Roo would provide a structured framework for comparing the performance of different AI models on standardized programming tasks. This system would leverage Roo's new_task tool to create a hierarchical testing architecture that would enable systematic evaluation of AI capabilities.

Beyond simple performance comparisons, the results from this benchmarking system would provide programmers with valuable feedback for:

1. System Instruction Tuning: Programmers could identify optimal system prompts and instructions by analyzing how different formulations impact model performance across various tasks.

2. Tooling Error Detection: Developers could systematically detect and categorize errors in tool usage (ie, apply_diff), gaining insights for improving tool design and error handling across models and with model or model-class specific instructions.

3. Continuous Integration Feedback: Roo dev teams could integrate model performance testing into development workflows, helping ensure that code changes don't negatively impact AI capabilities.

4. AI Implementation Performance: Roo's team could quantify and track improvements in AI model performance over time, obtaining objective metrics for evaluating new models and configurations.

Overview

This document outlines the design for an AI model benchmarking system integrated into Roo. The system leverages Roo's subtask architecture to create a hierarchical testing framework that can compare multiple AI models across standardized programming tasks.

Continuous Integration and Development Applications

System Instruction Optimization

The benchmarking system would enable programmers to test variations of system instructions to identify optimal configurations:

1. A/B Testing: Programmers could compare different instruction formulations on the same tasks
2. Instruction Evolution: Teams could iteratively refine instructions based on performance metrics
3. Context Window Optimization: Developers could identify the optimal amount of context for different tasks

Benchmark Types

Based on our discussion, the benchmarking system focuses on three key types of benchmarks:

1. HumanEval

• Based on OpenAI's HumanEval benchmark
• Tests the model's ability to generate functionally correct code from docstrings
• Measures Pass@k metrics (k=1, 5, 10)
• Evaluates code correctness through test case execution
• Provides insights into code generation capabilities

2. Code Understanding

• Tests the model's ability to comprehend and explain existing code
• Evaluates accuracy of explanations
• Measures identification of key components
• Assesses comprehension of complex patterns
• Provides insights into code comprehension capabilities

3. Tool Usage

• Evaluates the model's efficiency in using Roo's tools
• Measures tool selection appropriateness
• Assesses number of tool invocations needed
• Evaluates task completion success rate
• Provides insights into practical problem-solving capabilities

These benchmarks provide a comprehensive evaluation of the key capabilities required for effective programming assistance, focusing on the core strengths needed in an AI coding assistant.

Architecture

The benchmarking system uses a hierarchical structure of tasks and subtasks:

Main Benchmark Task
├── Model A Subtask
│   ├── Benchmark 1 Subtask
│   ├── Benchmark 2 Subtask
│   └── Benchmark 3 Subtask
├── Model B Subtask
│   ├── Benchmark 1 Subtask
│   ├── Benchmark 2 Subtask
│   └── Benchmark 3 Subtask
└── Results Collection Subtask

Key Components

1. Benchmark Coordinator: Orchestrates the entire benchmarking process
2. Model Subtasks: Configures and runs tests for specific AI models
3. Benchmark Subtasks: Executes standardized tests against configured models
4. Results Collector: Aggregates and analyzes benchmark results

Subtask Creation Flow

The system uses Roo's new_task tool to create a hierarchy of subtasks:

1. Main Task: Initiated by the user, configures benchmark parameters
2. Model Tasks: One subtask per model, configures the model
3. Benchmark Tasks: One subtask per benchmark per model, runs tests
4. Results Task: Collects and analyzes results from all benchmarks

Benchmark Suites

1. HumanEval

Based on OpenAI's HumanEval benchmark, tests the model's ability to generate functionally correct code from docstrings.

Metrics:

• Pass@k (k=1, 5, 10)
• Average completion time
• Token efficiency

2. Code Understanding

Tests the model's ability to comprehend and explain existing code.

Metrics:

• Accuracy of explanations
• Identification of key components
• Comprehension of complex patterns

3. Tool Usage

Evaluates the model's efficiency in using Roo's tools to accomplish tasks.

Metrics:

• Tool selection appropriateness
• Number of tool invocations
• Task completion success rate

4. Refactoring

Tests the model's ability to improve existing code.

Metrics:

• Code quality improvement
• Preservation of functionality
• Efficiency of refactored code

User Experience

Initiating Benchmarks

Users can start a benchmark from the chat interface:

I want to benchmark Claude 3 Opus and GPT-4o on HumanEval and code understanding tasks

The system will:

1. Parse the request to identify models and benchmarks
2. Create the main benchmark task
3. Spawn subtasks for each model/benchmark combination
4. Display progress updates in the chat

Viewing Results

Results are presented in multiple formats:

1. Summary Table: Comparative overview of model performance
2. Detailed Reports: In-depth analysis of each benchmark
3. Visualizations: Charts comparing key metrics
4. Exportable Data: JSON files for further analysis

Example summary:

📊 Benchmark Results: 2 Models, 2 Benchmarks

HumanEval Pass@1:
- Claude 3 Opus: 78.2%
- GPT-4o: 81.5%

Code Understanding Accuracy:
- Claude 3 Opus: 92.3%
- GPT-4o: 89.7%

Average Response Time:
- Claude 3 Opus: 12.3s
- GPT-4o: 9.7s

Leveraging new_task for Benchmarking

The benchmarking system makes extensive use of Roo's new_task tool to create and manage the hierarchy of benchmark tasks:

// Example of creating a benchmark subtask using new_task
<new_task>
<mode>code</mode>
<message>Run HumanEval benchmark for ${modelName}.
Test cases: ${JSON.stringify(testCases)}
Output results to: ${outputPath}
</message>
</new_task>

[cte]

I like the idea of using new_task for this 💪. Yesterday I did a full benchmark run using https://github.com/cte/Roo-Code-Benchmark and the full run took over 7 hours to complete, so I think for the purposes of benchmarking each Roo Code release we'll probably use GitHub actions to parallelize the tasks in the cloud.

[KJ7LNW]

Roo Code: Percent Correct -> 91.8%, Cost -> $36.57
Aider: Percent Correct -> 60.4%, Cost -> $17.72

Phenomenal results! I assume the model was the same but just to verify, because +30% is substantial: Was aider using sonnet 3.5 or 3.7?

By the way you might consider adding a model to that table. Would be interesting to see how this performs across other models.

[KJ7LNW]

You could also add Aider baseline values to the table per-model, if you have them, to facilitate reporting.

[cte]

Phenomenal results! I assume the model was the same but just to verify, because +30% is substantial: Was aider using sonnet 3.5 or 3.7?

By the way you might consider adding a model to that table.

👍 - The details of how Roo is configured (including the model used) is in the runs table.

[KJ7LNW]

Are there benchmark results for other AI tools on the market that produce run the same polyglot benchmark, or at this point is it only Aider and Roo? It would be interesting to compare to other tools, if they exist.

👍 - The details of how Roo is configured (including the model used) is in the runs table.

It may be possible to use your benchmark tool to tune system instructions to make apply_diff more reliable: Do you have stats on apply_diff retries, and write_to_file counts when the file already exists and it should have been able to use apply_diff? The tool could even be self tuning, and ultimately reduce AI costs through fewer responses, retries, and avoiding write_to_file because it is expensive (high output tokens).

For example:

1. run a benchmark
2. collect stats about where apply_diff failed
3. ask the engine to generate system instructions to prevent those failures in the future
4. iterate

[cte]

Are there benchmark results for other AI tools on the market that produce run the same polyglot benchmark, or at this point is it only Aider and Roo? It would be interesting to compare to other tools, if they exist.

Totally agree. I'd love it if all these tools had a standard interface for running benchmarks that are similar to Aider's. It's probably possible to hack some of them and get some results, but I haven't spent any time on it.

It may be possible to use your benchmark tool to tune system instructions to make apply_diff more reliable: Do you have stats on apply_diff retries, and write_to_file counts when the file already exists and it should have been able to use apply_diff? The tool could even be self tuning, and ultimately reduce AI costs through fewer responses, retries, and avoiding write_to_file because it is expensive (high output tokens).

I think this is the real win that we can get out of the benchmarks. I want to do everything you just said and more :). Stay tuned!

[dbsxdbsx]

Your post just remind me.... what if let roo code to recommended its own best llm for each mode with roo code official its own llm benchmark as the Aider does?
Or what if even aggressive, let aider cooperate with roo code, let the 2 becomes the same "company"? since roo code seems the most similar with aider ,but in a addon format.