You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
Repository navigation
how to interpret results for Auto Code Rover SWE-bench? #47
cost_X_Y: X is the budget cost of running swe-agent in our experiment, and Y is the trail of repetition.
In this case, we used a budget of 2 USD, and repeated the experiment 3 times.
Inside the cost_X_Y directory, *.traj files are the conversation log files for each task instance in swe-bench.
all_pred.jsonl includes all the generated patches.
For AutoCodeRover acr-run-1, acr-run-2, and acr-run-3 results align with Table-3, In our environment, the ACR column.
generated: there is an agent-generated patch for this issue
with_logs: a log file is produced when executing the passing/failing test-cases of this issue
applied: the patch can be applied successfully to the original program.
resolved: the patch made the passing/failing test-cases of this issue pass
Activity
ramsey-coding commentedon May 14, 2024
what's the difference between the following fields?
zhiyufan commentedon May 16, 2024
cost_X_Y: X is the budget cost of running swe-agent in our experiment, and Y is the trail of repetition.
In this case, we used a budget of 2 USD, and repeated the experiment 3 times.
Inside the cost_X_Y directory, *.traj files are the conversation log files for each task instance in swe-bench.
all_pred.jsonl includes all the generated patches.
For AutoCodeRover acr-run-1, acr-run-2, and acr-run-3 results align with Table-3, In our environment, the ACR column.
The details on how the stats are generated can be found here: https://github.com/yuntongzhang/SWE-bench/blob/main/metrics/report.py#L264C5-L264C21