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| Aider is SOTA for both SWE Bench and SWE Bench Lite | Aider sets SOTA for the main SWE Bench, after recently setting SOTA for the Lite version. | /assets/swe_bench.jpg | true |
Aider is SOTA for both SWE Bench and SWE Bench Lite
Aider scored 18.8% on the main SWE Bench benchmark, achieving a state-of-the-art result. The current top leaderboard entry is 13.8% from Amazon Q Developer Agent. The best result reported elsewhere seems to be 13.9% from Devin.
This result on the main SWE Bench is in addition to aider's SOTA result on the easier SWE Bench Lite that was reported recently.
Aider was benchmarked on 570 of the 2294 SWE Bench problems. These were the same randomly selected 570 problems that Devin used in their evaluation. Please see the references for more details on the data presented in this chart.
Interactive, not agentic
Aider achieved this result mainly through its existing features that focus on static code analysis, reliable LLM code editing, and pragmatic UX for automatically fixing linting and testing errors. Aider intentionally has quite limited and narrow "agentic behavior" to avoid long delays, high token costs and the need for users to repeatedly code review incorrect solutions. It's also worth noting that aider currently does not use RAG, vector search, tools or give the LLM access to search the web or unilaterally execute code.
Aider is first and foremost an interactive tool for engineers to get real work done in real code bases using a chat interface. Aider provides a pair programming UX where users can ask for a change and see the edits performed in real-time. Aider can also offer additional help like fixing lint or test errors, but the user is always in full interactive control. This allows them to quickly steer misunderstandings back on course and avoid wasting time and token costs.
Benchmark methodology
Benchmarking was conducted as follows:
- Aider with GPT-4o was launched in each problem's git repository with the problem statement submitted as the opening chat message from "the user".
- After that aider ran as normal, except all of 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 reported that it had successfully edited the repo without causing syntax errors or breaking any pre-existing tests.
- If the solution from aider with GPT-4o wasn't plausible, the harness launched aider to try again from scratch using Claude 3 Opus.
- If no plausible solution was found after those two tries, the harness picked the "most plausible" solution with the fewest edit/lint/test problems.
It's important to be clear that aider and the benchmark harness only had access to the pre-existing tests in each problem's repo. The held out "acceptance tests" were only used after benchmarking to compute statistics on which problems aider correctly resolved.
This is the same approach that was used for aider's recent SOTA result on SWE Bench Lite. For the Lite benchmark, aider alternated between GPT-4o and Opus for up to 6 total attempts. Due to the increased token costs involved in running the main SWE Bench benchmark, aider was limited to 2 total attempts: one attempt of aider with GPT-4o and one with Opus.
The problems from the main SWE Bench dataset are more difficult and involved edits to multiple source files, which increased the token costs as compared to Lite. Further, aider was benchmarked on 570 SWE Bench problems versus only 300 Lite problems, adding another factor of ~two to the costs.
For a detailed discussion of the benchmark methodology, please see the article about aider's SWE Bench Lite results. Also, the aider SWE Bench repository on GitHub contains the harness and statistics code used for the benchmarks.
The benchmarking process was similar to how a developer might use aider to resolve a GitHub issue:
- They could launch aider in their repo with the command below, which
tells aider they want to accept every suggestion
and to use pytest to run tests.
aider --yes --test-cmd pytest
- They could start the chat by pasting in the URL or text of a GitHub issue. Aider will pull in the URL's content and then try and solve the issue.
- 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, so it's always easy to revert AI changes that don't pan out.
Aider with GPT-4o alone was SOTA
Using aider with GPT-4o to make a single attempt at solving each problem achieved a score of 17.0%. 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.
Aider with GPT-4o & Opus
The benchmark harness started by using aider with GPT-4o to try and solve each problem. For problems where this didn't produce a plausible solution, the harness tried again using aider with Opus. So at most, two attempts were made for each problem.
The table below breaks down the proposed solutions that were found from each attempt at the 570 problems. A proposed solution is either:
- A plausible solution where aider reported no outstanding errors from editing, linting and testing.
- Or, the "most plausible" solution generated by either attempt, with the fewest outstanding editing, linting or testing errors.
The table also provides details on the 107 solutions that were ultimately verified as correctly resolving their issue.
| Attempt | Agent | Number of proposed solutions |
Percent of proposed solutions |
Number of correctly resolved solutions |
Percent of correctly resolved solutions |
Score on SWE Bench Lite |
|---|---|---|---|---|---|---|
| 1 | Aider with GPT-4o | 419 | 73.5% | 87 | 81.3% | 15.3% |
| 2 | Aider with Opus | 151 | 26.5% | 20 | 18.7% | 3.5% |
| Total | 570 | 100% | 107 | 100% | 18.8% |
Non-plausible but correct solutions?
A solution doesn't have to be plausible in order to correctly resolve the issue. Recall that plausible is simply defined as aider reporting that it successfully completed all file edits, repaired and resolved any linting errors and resolved any test failures. But there are many reasons why aider might fail to do those things and yet still produce a solution that will pass acceptance testing:
- There may have been pre-existing failing tests in the repo, before aider even started working on the SWE Bench problem. Aider may not have resolved such issues, and yet they may not be relevant to the acceptance testing. The SWE Bench acceptance testing just confirms that tests pass or fail in the same pattern as the "gold patch" developed by a human to solve the problem. Some tests may fail during acceptance testing, and that's ok as long as they failed for the gold patch too.
- There may have been pre-existing linting problems in the repo. If lingering linting issues affected code paths that are not well tested, they may not impact acceptance testing.
- Aider may have reported file editing errors because it thought the LLM specified edits that it wasn't able to successfully apply. This can only happen when the LLM specified edits in a way that doesn't comply with the editing instructions in the system prompt. Given that the LLM isn't complying with the system prompt, it may have become confused and asked for redundant or otherwise irrelevant edits. Such outstanding edit errors might not be fatal for acceptance testing.
- Etc.
Keeping all this in mind, we can understand why GPT-4o accounts for 15.3% of the benchmark score in the table above, but benchmarking with just one attempt of aider with GPT-4o scored 17.0%. When an Opus attempt is allowed after GPT-4o, it may propose some incorrect solutions which are "more plausible" than some of GPT-4o's non-plausible solutions. These more plausible, incorrect solutions can eclipse some of the earlier non-plausible correct solutions that GPT-4o generated. This reduces GPT-4o's score in the table (15.3%) from the combined GPT-4o & Opus benchmark, as compared to the results from just one try using aider with GPT-4o (17.0%).
For these reasons, adding additional attempts is not guaranteed to monotonically increase the number of resolved problems. New solutions may solve some new problems but they may also eclipse and discard some of the previous non-plausible correct solutions. Luckily, additional attempts usually provide a net increase in the overall number of resolved solutions. This was the case for both this main SWE Bench result and the earlier Lite result.
The table below breaks down the benchmark outcome of each problem, showing whether aider with GPT-4o and with Opus produced plausible and/or correct solutions.
| Row | Aider w/GPT-4o solution plausible? |
Aider w/GPT-4o solution resolved issue? |
Aider w/Opus solution plausible? |
Aider w/Opus solution resolved issue? |
Number of problems with this outcome |
Number of problems resolved |
|---|---|---|---|---|---|---|
| A | plausible | resolved | n/a | n/a | 73 | 73 |
| B | plausible | not resolved | n/a | n/a | 181 | 0 |
| C | non-plausible | resolved | plausible | resolved | 1 | 1 |
| D | non-plausible | resolved | plausible | not resolved | 2 | 0 |
| E | non-plausible | resolved | non-plausible | resolved | 16 | 16 |
| F | non-plausible | resolved | non-plausible | not resolved | 5 | 3 |
| G | non-plausible | not resolved | non-plausible | resolved | 4 | 2 |
| H | non-plausible | not resolved | non-plausible | not resolved | 216 | 0 |
| I | non-plausible | not resolved | plausible | resolved | 12 | 12 |
| J | non-plausible | not resolved | plausible | not resolved | 53 | 0 |
| K | non-plausible | not resolved | n/a | n/a | 7 | 0 |
| Total | 570 | 107 |
Rows A-B show the cases where aider with GPT-4o found a plausible solution during the first attempt. Of those, 73 went on to be deemed as resolving the issue, while 181 were not in fact correct solutions. The second attempt with Opus never happened, because the harness stopped once a plausible solution was found.
The remaining rows consider cases where aider with GPT-4o did not find a plausible solution, so Opus got a turn to try and solve. Rows C-F are cases where GPT-4o's non-plausible solutions were actually found to be correct in hindsight. In row D we can see the cases where aider with Opus definitely overrides 2 of them with plausible-but-incorrect solutions.
In rows E-H we can see that both GPT-4o and Opus produced non-plausible solutions. Which one was ultimately selected for each problem depends on details about which solution the harness considered "most plausible".
Rows I-J consider the straightforward cases where aider with GPT-4o didn't find a plausible solution but Opus did. Of these, Opus' solution went on to be deemed correct for 12 problems and incorrect for 53.
Row K contains cases where Opus returned errors due to context window exhaustion or other problems. In these cases aider with Opus was unable to produce any solutions.
Computing the benchmark score
The benchmark harness produced one proposed solution for each of the 570 SWE Bench problems.
A separate evaluation script was used to test each of these solutions with the full test suite, including the held out acceptance tests. For this final acceptance testing, any edits that aider made to tests were discarded. This ensured that the correct, unmodified test suite was used for acceptance testing. The evaluation script compared each proposed solution's test results with results from testing the "gold" patch that was developed by a human to correctly solve the issue. If they matched, the proposed solution correctly resolved the issue.
These acceptance tests were only ever run outside of aider and the benchmark harness, and only to compute statistics about the correctly resolved instances. They were never run, used, or even visible during aider's attempts to solve the problems.
Aider correctly resolved 107 out of 570 SWE Bench instances that were benchmarked, or 18.8%.
Acknowledgments
Much thanks to the team behind the SWE Bench family of AI coding benchmarks. Also thanks to Albert Örwall who has dockerized the SWE Bench evaluation scripts making it faster, easier, and more reliable to run the acceptance tests.
References
Below are the references for the SWE-Bench results displayed in the graph at the beginning of this article.
- 13.9% Devin (benchmarked on 570 instances)
- 13.8% Amazon Q Developer Agent (benchmarked on 2294 instances)
- 12.5% SWE- Agent + GPT-4 (benchmarked on 2294 instances)
- 10.6% AutoCode Rover (benchmarked on 2294 instances)
- 10.5% SWE- Agent + Opus (benchmarked on 2294 instances)
The graph contains average pass@1 results for AutoCodeRover.
The AutoCodeRover GitHub page
features their pass@3 results
without being clearly labeled.
Table 2 of their
paper
reports an ACR-avg result of 10.59% which is an average pass@1 result.
The results presented here for aider are all pass@1, as the official SWE Bench leaderboard only accepts pass@1 results.