aider/docs/benchmarks.md

GPT code editing benchmarks

Aider is an open source command line chat tool that lets you ask GPT to edit code in your local git repos. You can use aider to ask GPT to add features, write tests or make other changes and improvements to your code.

The ability for GPT to reliably edit local source files is crucial for this functionality. Improving the reliability of code editing often involves modifying and experimenting with the "edit format" used by aider. The edit format is a critical component of the system prompt, dictating how GPT should structure code edits in its responses.

Aider currently uses simple text based editing formats, but OpenAI's new function calling API looked like a promising way to construct a more structured editing format. Before making such a big change, I wanted to make sure I had a quantitative way to assess the impact on the reliability of code editing.

I developed a benchmark based on the Exercism python coding exercises. This benchmark evaluates how effectively aider and GPT can translate a natural language coding request into actual runnable code saved into files that pass unit tests. It's an end-to-end evaluation of not just GPT's code writing ability, but also its capacity to edit existing code and package those code changes so that aider can save the edits to the local source files.

I ran this code editing benchmark on almost all the ChatGPT models, using a variety of edit formats. The results were quite interesting:

• Asking GPT to return an updated copy of the whole file in a standard markdown fenced code block proved to be the most reliable and effective edit format across all GPT-3.5 and GPT-4 models. The results from this whole edit format are shown in solid blue in the graph.
• Using the new function calling API performed worse than the above whole file method for all models. GPT-3.5 especially produced inferior code and frequently mangled this output format. This was surprising, as the functions API was introduced to enhance the reliability of structured outputs. The results from these ...-func edit methods are shown as patterned bars in the graph (both green and blue).
• As expected, the GPT-4 models outperformed the GPT-3.5 models in code editing.

The quantitative benchmark results align with my intuitions about prompting GPT for complex tasks like coding. It's beneficial to minimize the "cognitive overhead" of formatting the response, allowing GPT to concentrate on the task at hand. As an analogy, imagine asking a junior developer to implement a new feature by manually typing the required code changes as diff -c formatted edits. You wouldn't expect a good result.

Using more complex output formats seems to introduce two issues:

• It makes GPT write worse code. Keeping the output format simple appears to allow GPT to devote more attention to the actual coding task.
• It reduces GPT's adherence to the output format, making it more challenging for tools like aider to accurately identify and apply the edits GPT is attempting to make.

I was planning to start using a function call based edit format in aider for both GPT-3.5 and GPT-4. But given these benchmarking results, I won't be adopting the functions API at this time.

More details on the benchmark, edit formats and results are discussed below.

The benchmark

The benchmark uses 133 practice exercises from the Exercism python repository. These exercises were designed to help individuals learn Python and hone their coding skills.

Each exercise includes:

• Instructions for the exercise, provided in markdown files.
• Stub code for the implementation in a python file, specifying the functions/classes that need to be implemented.
• Unit tests in a seperate python file.

The goal is for GPT to read the instructions, implement the provided functions/class skeletons and pass all the unit tests. The benchmark measures what percentage of the 133 exercises are completed successfully, causing all the associated unit tests to pass.

To complete an exercise, aider sends GPT the initial contents of the implementation file, the Exercism instructions and a final instruction:

Use the above instructions to modify the supplied files: <implementation file>
Keep and implement the existing function or class stubs, they will be called from unit tests.
Only use standard python libraries, don't suggest installing any packages.

Aider updates the implementation file based on GPT's reply and runs the unit tests. If all tests pass, the exercise is considered complete. If some tests fail, Aider sends GPT a second message with the test error output. It only sends the first 50 lines of test errors to avoid exceeding the context window of the smaller models. Aider also includes this final instruction:

See the testing errors above.
The tests are correct.
Fix the code in <implementation file> to resolve the errors.

Requiring GPT to fix its first implementation in response to test failures is another way in which this benchmark stresses code editing skill. This second chance is also important because it gives a chance for GPT to adjust if the instructions were imprecise with respect to the specific requirements of the unit tests. Many of the exercises have multiple paragraphs of instructions, and most human coders would likely fail some tests on their first try.

It's worth noting that GPT never gets to see the source code of the unit tests during the benchmarking. It only sees the error output from failed tests. Of course, all of this code was probably part of its original training data!

In summary, passing an exercise means GPT was able to:

• Write the required code (possibly after reviewing test error output),
• Correctly package all of this code into the edit format so that Aider can process and save it to the implementation file.

Conversely, failing an exercise only requires a breakdown in one of those steps. In practice, GPT fails at different steps in different exercises. Sometimes it simply writes the wrong code. Other times, it fails to format the code edits in a way that conforms to the edit format, resulting in the code not being saved correctly.

It's worth keeping in mind that changing the edit format often affects both aspects of GPT's performance. Complex edit formats often lead to poorer code and make it less successful at formatting the edits correctly.

Edit formats

I benchmarked 4 different edit formats, described below. Each description includes a sample response that GPT might provide in response to a user who requests: "Change the print from hello to goodbye."

whole

The whole format asks GPT to return an updated copy of the entire file, including any changes. The file should be formatted with normal markdown triple-backtick fences, inlined with the rest of its response text.

This format is very similar to how ChatGPT returns code snippets during normal chats, except with the addition of a filename right before the opening triple-backticks.

Here is the updated copy of your file demo.py:

demo.py
```python
def main():
    print("goodbye")
```

diff

The diff format also asks GPT to return edits as part of the normal response text, in a simple diff format. Each edit is a fenced code block that specifies the filename and a chunk of ORIGINAL and UPDATED code. GPT provides some original lines from the file and then a new updated set of lines.

Here are the changes you requested to demo.py:

```python
demo.py
<<<<<<< ORIGINAL
    print("hello")
=======
    print("goodbye")
>>>>>>> UPDATED
```

whole-func

The whole-func format requests updated copies of whole files to be returned using the function call API.

{
    "explanation": "Changed hello to goodbye.",
    "files": [
        {
            "path": "demo.py",
            "content": "def main():\n    print(\"goodbye\")\n"
        }
}

diff-func

The diff-func format requests a list of original/updated style edits to be returned using the function call API.

{
    "explanation": "Changed hello to goodbye.",
    "edits": [
        {
            "path": "demo.py",
            "original_lines": [
                "    print(\"hello\")"
            ],
            "updated_lines": [
                "    print(\"goodbye\")"
            ],
        }
    ]
}       

GPT-3.5 struggles with complex edit formats

While GPT-3.5 can pass some exercises using edit formats other than the whole format, it struggles with the rest of the formats.

Pathlogical use of diff

While GPT-3.5 can sometimes correctly generate the diff edit format, it often uses it in a pathological manner. It places the entire original source file in the ORIGINAL block and the entire updated file in the UPDATED block. This is strictly worse than just using the whole edit format, as GPT is sending 2 full copies of the file.

Hallucinating function calls

When using the functions API GPT-3.5 is prone to ignoring the JSON Schema that specifies valid functions. It often returns a completely novel and semantically invalid function_call fragment with "name": "python".

        "function_call": {
          "name": "python",
          "arguments": "def main():\n    print(\"hello\")\n"
        },

The arguments attribute is supposed to be a set of key/value pairs with the arguments to the function specified in the name field. Instead, GPT-3.5 frequently just stuffs an entire python file into that field.

It seems like it might be getting confused by fine-tuning that was done for the ChatGPT code interpreter plugin?

Randomness

The benchmark attempts to be deterministic, always sending identical requests for each exercise on repeated runs. As part of this effort, when sending test error output to GPT, it removes the wall-clock timing information that is normally included by the unittest module.

The benchmarking harness also logs SHA hashes of all the OpenAI API requests and replies. This makes it possible to detect randomness or nondeterminism in the bechmarking process.

It turns out that the OpenAI chat APIs are not deterministic, even at temperature=0. The same identical request will produce multiple distinct responses, usually less than 5-10 variations. This suggests that OpenAI may be load balancing their API across a number of slightly different instances of the model?

For some exercises, some of these variable responses pass the unit tests while other variants do not. Results for exercises like this, which are "on the bubble", are therefore a bit random, depending on which variant OpenAI returns.

Given that, it would be ideal to run all 133 exercises many times for each model/edit-format combination and report an average performance. This would average away the effect of the API variance. It would also significantly increase the cost of this sort of benchmarking. So I didn't do that.

Benchmarking against 133 exercises provides some robustness all by itself, since we are measuring the performance across many exercises.

But to get a sense of how much the API variance impacts the benchmark outcomes, I ran all 133 exercises 10 times each against gpt-3.5-turbo-0613 with the whole edit format. You'll see one set of error bars in the graph, which show the range of results from those 10 runs.

The OpenAI API randomness doesn't seem to cause a large variance in the overall benchmark results.

Conclusions

Based on these benchmarking results, aider will continue to use the whole edit format for GPT-3.5, and diff for GPT-4.