aider/_posts/2024-05-22-swe-bench-lite.md

---
title: Aider scores SOTA 26.3% on SWE Bench Lite
excerpt: Aider scored 26.3% on SWE Bench Lite, achieving a state of the art result.
highlight_image: /assets/swe_bench_lite.jpg
draft: true
---

# Aider scores SOTA 26.3% on SWE Bench Lite
 
Aider scored 26.3%
on the
[SWE Bench Lite benchmark](https://www.swebench.com), achieving a state-of-the-art result. 
The current top leaderboard entry is 20.3%
from Amazon Q Developer Agent.
The best result reported elsewhere online seems to be
[22.3% from AutoCodeRover](https://github.com/nus-apr/auto-code-rover).

[![SWE Bench Lite results](/assets/swe_bench_lite.svg)](https://aider.chat/assets/swe_bench_lite.svg)

## Interactive, not agentic

Aider achieved this result mainly through its focus on static code analysis,
reliable LLM code editing,
and pragmatic workflows for interactive pair programming with AI.
Aider intentionally has quite limited and narrow "agentic behavior":
it doesn't require a highly detailed upfront "spec" from the user,
use RAG or vector search, farm out sub-problems to an army of LLMs,
allow the LLM to use tools,
or perform web searches,
etc.

Aider is first and foremost a tool for engineers to get real work done in
real code bases through a pair programming chat style interface.
When a user asks aider for a change, they see the edits performed in real-time,
and aider may also then offer additional
help like fixing lint or test errors.
In normal use, the user is in full interactive control. 
This lets them quickly steer misunderstandings back on course and
avoid wasted time, code reviews and token costs.


## Benchmark methodology

For the benchmark, 
aider was launched in each problem's git repository
with the problem statement
submitted as the opening chat message from "the user."
After that aider runs as normal, with the following modifications:

- Aider's suggestions were always accepted without user approval.
- A simple harness was used to retry the SWE Bench problem if aider produced code that wasn't *plausibly correct*.
Plausibly correct means that aider concluded that it had successfully edited the repo
without causing syntax errors or breaking any *pre-existing* tests.
- If the solution isn't plausible, the harness launches aider to try again from scratch,
alternating between using aider with GPT-4o and Opus.
- If no plausible solution is found after six tries, the harness picks the solution
with the least amount of edit/lint/test problems.

It's important to be clear that during benchmarking
*aider only had access to the pre-existing tests in the repo*.
It could not see or run the held out "acceptance tests" that are used later to see if the
SWE Bench problem was correctly resolved.

The benchmarking process was similar to a user employing aider like this:

- Launching aider in their repo with the command below, which
tells aider to automatically proceed with every suggestion
and use pytest to run tests.
  - `aider --yes --test-cmd pytest`
- Pasting the text of a GitHub issue into the chat, or adding it via URL with a command in the chat like:
  - `/web https://github.com/django/django/issues/XXX`
- If aider doesn't produce code that lints and tests clean, the user might decide to revert the changes and try again, maybe using aider with a different LLM this time.
[Aider is tightly integrated with git](https://aider.chat/docs/faq.html#how-does-aider-use-git),
so it's always easy to revert AI changes that don't pan out.

Outside a benchmark setting, it's probably
unwise to let *any* AI agent run unsupervised on your code base.
Aider is intended to be used as an interactive pair-programming chat,
where the user participates to direct aider's work and approve suggestions.
This way the user can offer immediate feedback or corrections if their initial
instructions turn out to be ambiguous,
or if the AI starts going down a wrong path.

## Aider with GPT-4o alone was SOTA

Running the SWE Bench Lite benchmark using aider with just GPT-4o
achieved a score of 25%.
This was itself a state-of-the-art result, before being surpassed by the main
result being reported here
that used aider with both GPT-4o & Opus.

## GPT-4o vs Opus

The benchmark harness alternated between running aider with GPT-4o and Opus.
The harness proceeded in a fixed order, always starting with GPT-4o and
then alternating with Opus until a plausible solution was found.

The table below breaks down the 79 solutions that were ultimately
verified as correctly resolving their issue.
Some noteworthy observations:

- Aider with GPT-4o immediately found 77% of the valid solutions on the first attempt.
- ~90% of valid solutions were found after one attempt from aider with GPT-4o and Opus.
- A long tail of solutions continued to be found by both models including one on the final, sixth attempt of that problem.


| Attempt | Agent      | Number<br/>resolved | Percent<br/>of resolved | Cumulative<br/>percent of<br/>resolved |
|:--------:|------------|---------:|---------:|----:|
| 1 | Aider with GPT-4o | 61 | 77.2 | 77.2
| 2 | Aider with Opus | 10 | 12.7 | 89.9
| 3 | Aider with GPT-4o |  3 |  3.8 | 93.7
| 4 | Aider with Opus |  2 |  2.5 | 96.2
| 5 | Aider with GPT-4o |  2 |  2.5 | 98.7
| 6 | Aider with Opus |  1 |  1.3 | 100.0
|**Total**|   | **79** | **100%** | **100%** |

If we break down correct solutions purely by model,
we can see that GPT-4o dominates.
This isn't a fair and direct comparison, because GPT-4o always took the first
turn at solving.
But anecdotal evidence from earlier runs of the benchmark
supports the observation that aider with GPT-4o is significantly stronger than Opus
for this endeavor.

| Agent      | Number resolved | Percent of resolved | 
|------------|---------:|---------:|
| Aider with GPT-4o | 66 | 83.5 |
| Aider with Opus | 13 | 16.5 |
|**Total**| **79** | **100%** |


## Repository map, not RAG

The crucial first step in solving a SWE Bench problem is figuring out
which parts of the repo are relevant and which files need to be edited.
Most coding agents use some combination of RAG, vector search
and arming the LLM with
tools to interactively explore the code base.

Aider instead uses a
[repository map](https://aider.chat/2023/10/22/repomap.html)
to help the LLM understand the 
layout, code structure, and content of a git repo.
The repo map is created from the code's AST and call graph
to provide a compact and powerful summary of the entire code base.
The map is constantly
tailored to show
repo context that is relevant to the current state of the chat conversation.
This is done by performing a graph optimization on the code's call graph.

When the user asks for a change to their code, the LLM uses the repo map
to decide which files to edit.
The LLM simply returns a normal text response explaining which files
it needs to edit and why.
Aider notices when the LLM mentions filenames from the repo,
and asks the user if they should be added to the chat.
Adding a file to the chat allows the LLM to see the full contents
of the file and edit it.

<div class="chat-transcript" markdown="1">

#### Please add a new /factorial/N endpoint.

To add a new /factorial/N endpoint, the most likely file that needs to be edited is app.py.
Please add app.py to the chat so I can proceed with the changes.

> app.py  
> Add these files to the chat? yes

</div>

This is a convenient and natural workflow for interactive chat,
and it worked well for the SWE Bench tasks.
Each task comes with a “gold” patch, which was created by a human developer
to solve the issue. 
Aider successfully identified and added the file from the gold patch
in 70.3% of the benchmark tasks.

Of course aider is not able to see or use the gold patch
or the files it names in any way. 
They were only used to compute this statistic after the benchmarking was completed. 


## Reliable code editing

Once files have been selected for editing,
the next step is of course to edit the source code to fix the problem.

Aider has always had a deep focus on ensuring that LLMs can not just write code,
but reliably *edit* code.
Aider has a collection of prompting strategies and code editing backends which have
been honed through
[extensive benchmarking](https://aider.chat/docs/leaderboards/).
These foundational capabilities help ensure that the LLM can not only code up a solution but
also properly integrate it into the existing code base and source files.

The repository map helps here too, making sure that the LLM
can see relevant classes, functions and variables from the entire repo.
This helps ensure that the project's existing APIs and conventions are
respected when new code is added.

## Linting and fixing

[Aider lints code](https://aider.chat/2024/05/22/linting.html)
after every LLM edit and offers to automatically fix
any linting errors.
Aider includes basic linters built with tree-sitter to check
[most popular programming languages](https://github.com/paul-gauthier/grep-ast/blob/main/grep_ast/parsers.py).
These built in linters will detect syntax errors and other fatal problems with the code.

Users can also configure aider to use their preferred linters.
This allows aider to check for a larger class of problems, keep the code style
aligned with the rest of the repo, etc.
But for the benchmark, aider simply used its built-in linters.

Aider shows linting errors to the LLM in a novel format,
using the abstract syntax tree (AST) to display relevant code context for each
error.
This context increases the ability of the LLM to understand the problem and
make the correct changes to resolve it.

<div class="chat-transcript" markdown="1">

```
app.py:23:36: F821 undefined name 'num'  
app.py:41:16: F541 f-string is missing placeholders  
  
app.py:  
...⋮...  
  6│class LongNum:  
  7│    def __init__(self, num):  
  8│        """  
  9│        Initialize the number.  
 10│        """  
...⋮...  
 19│    def __str__(self):  
 20│        """  
 21│        Render the number as a string.  
 22│        """  
 23█        return str(num)  
 24│  
 25│  
 26│@app.route('/subtract/<int:x>/<int:y>')  
...⋮...  
 38│@app.route('/divide/<int:x>/<int:y>')  
 39│def divide(x, y):  
 40│    if y == 0:  
 41█        return f"Error: Cannot divide by zero"  
 42│    else:  
 43│        result = x / y  
 44│        return str(result)  
 45│  
...⋮...  
```  

> Attempt to fix lint errors? yes

</div>


## Testing and fixing

Aider can be configured with the command needed to run tests for a repo.
A user working on a python project might do that by launching
aider like this:

```
aider --test-cmd pytest
``` 

The repositories that are used in the SWE Bench problems are large open
source projects with extensive existing test suites.
A repo's test suite can be run in three ways:

1. Run tests as they existed before trying to solve the problem, without any changes.
2. Run tests after aider has modified the repo.
So the pre-existing test cases are still present, but may have been modified by aider.
Aider may have also added new tests.
3. Run the final "acceptance tests" to judge if aider has successfully resolved the problem.
These tests include the unmodified pre-existing tests and
a held out set of tests (from the so called `test_patch`).

For the benchmark, aider is configured with a test command that will run the tests
as described in (2) above.
So testing will fail if aider has broken any pre-existing tests or if any new
tests that it created aren't passing.
If any tests fail, aider will automatically
share the test output with the LLM and ask it to 
try and resolve the test failures.

To be clear, *aider cannot run or even see the "acceptance tests"* from the `test_patch`
described in (3).
Those tests are only run outside of aider and the benchmark harness,
to compute the final benchmark score.
To do that,
the SWE Bench support code
verifies that the pre-existing and held out tests
pass as expected from a correct solution.
If so, the issue is marked as resolved.
For this final acceptance testing,
any aider edits to tests are discarded to ensure a faithful determination
of whether the issue was resolved.

## Finding a plausible solution

As aider executes, it notes the outcome of the editing, linting, and testing
steps.
When aider completes, it returns its final status as either:
succeeded with no errors remaining,
or ended without resolving all errors.

The benchmark harness uses these outcomes to determine if it has a plausible
solution to the current SWE Bench task.
A plausible solution is one where aider
returns saying that it 
edited the repo with no outstanding
edit, lint, or test errors.
In this case, aider's changes are taken as the proposed solution and recorded
as the SWE Bench `model_patch` to be evaluated later with the
`test_patch` "acceptance tests".

If the solution is not plausible, another
instance of aider is launched again from scratch on the same problem.
The harness alternates launching aider with GPT-4o and Opus to solve the problem,
and gives each model three attempts -- for a total of six attempts.
As soon as a plausible solution is found, it is accepted and the
harness moves on to the next SWE Bench instance.

It's worth noting that repositories may have lint or test errors present before aider even starts to edit them. Whether errors are caused by aider or were pre-existing, there will be instances where, after six tries, no plausible solution is obtained.

If all six attempts fail to produce a plausible solution,
then the "best" solution available is selected as the
`model_patch`.
Which of the non-plausible solutions to use is determined
by ignoring the testing outcome
and prioritizing solutions in the following order:

 - Pick a solution where editing and linting were completed successfully.
 - Pick a solution where editing was at least partially successful and linting succeeded.
 - Pick a solution where editing was successful.
 - Pick a solution where editing was at least partially successful.

## Computing the benchmark score

The benchmark harness produces one "best" solution for each of the 300
SWE Bench Lite instances and saves it as a `model_patch`.
A separate evaluation script uses the SWE Bench support code to
test each of these results with the acceptance tests.

These `test_patch` acceptance tests are only ever run outside of aider
and the benchmark harness, and only to compute the number of
correctly resolved instances.
They are never run, used, or even visible during the attempts to solve the problems.

Aider correctly resolved 79 out of 300 SWE Bench Lite instances, or 26.3%.

## Acknowledgments

Much thanks to the team behind the
[SWE Bench](https://www.swebench.com)
family of AI coding benchmarks.
Also thanks to Albert Örwall who has
[dockerized the SWE Bench evaluation scripts](SWE-bench-docker)
making it faster, easier, and more reliable to run the acceptance tests.