aider_bench

AiderBench Evaluation

This folder contains evaluation harness for evaluating agents on the Aider Editing Benchmark. This will allow us to develop better editing approach without running the full SWE-bench. The benchmark uses the RajMaheshwari/Exercism-Python Hugging Face dataset based on the Exercism python coding exercises.

Setup Environment and LLM Configuration

Please follow instruction here to setup your local development environment and LLM.

Start the evaluation

./evaluation/aider_bench/scripts/run_infer.sh [model_config] [git-version] [agent] [eval_limit] [eval-num-workers] [eval_ids]

• model_config, e.g. eval_gpt4_1106_preview, is the config group name for your LLM settings, as defined in your config.toml.
• git-version, e.g. HEAD, is the git commit hash of the OpenHands version you would like to evaluate. It could also be a release tag like 0.9.0.
• agent, e.g. CodeActAgent, is the name of the agent for benchmarks, defaulting to CodeActAgent.
• eval_limit, e.g. 10, limits the evaluation to the first eval_limit instances. By default, the script evaluates the entire Exercism test set (133 issues). Note: in order to use eval_limit, you must also set agent.
• eval-num-workers: the number of workers to use for evaluation. Default: 1.
• eval_ids, e.g. "1,3,10", limits the evaluation to instances with the given IDs (comma separated).

There are also following optional environment variables you can set:

export USE_UNIT_TESTS=true # if you want to allow the Agent to verify correctness using unittests. Default to false.
export SKIP_NUM=12 # skip the first 12 instances from the dataset

Following is the basic command to start the evaluation.

You can update the arguments in the script evaluation/aider_bench/scripts/run_infer.sh, such as --max-iterations, --eval-num-workers and so on:

• --agent-cls, the agent to use. For example, CodeActAgent.
• --llm-config: the LLM configuration to use. For example, eval_gpt4_1106_preview.
• --max-iterations: the max allowed number of iterations to run the evaluation. Default: 30.
• --eval-num-workers: the number of workers to use for evaluation. Default: 1.
• --eval-n-limit: the number of examples to evaluate. For example, 100.
• --eval-ids: the IDs of the examples to evaluate (comma separated). For example, "1,3,10".

./evaluation/aider_bench/scripts/run_infer.sh eval_gpt35_turbo HEAD CodeActAgent 100 1 "1,3,10"

Summarize Results

poetry run python ./evaluation/aider_bench/scripts/summarize_results.py [path_to_output_jsonl_file] [model_name]
# with optional SKIP_NUM
poetry run python SKIP_NUM=12 ./evaluation/aider_bench/scripts/summarize_results.py [path_to_output_jsonl_file] [model_name]

Full example:

poetry run python ./evaluation/aider_bench/scripts/summarize_results.py evaluation/evaluation_outputs/outputs/AiderBench/CodeActAgent/claude-3-5-sonnet@20240620_maxiter_30_N_v1.9/output.jsonl claude-3-5-sonnet@20240620

This will list the instances that passed and the instances that failed. For each instance, the corresponding set of test cases (which can vary for each instance) are run on the file edited by the agent. We consider an instance to be passed only if ALL test cases are passed. Sometimes even a single failed test case will cause the entire instance to be marked as failed.

You can inspect the test_results field in the output.jsonl file to find the exact outcome of the tests. If there are no syntax or indentation errors, you can expect to see something like "..F...EF..", where "." means the test case passed, "E" means there was an error while executing the test case and "F" means some assertion failed and some returned output was not as expected.

Visualization

If the required Python libraries are installed (matplotlib.pyplot and seaborn), the summarize_results.py script will also generate two histograms to the output folder.

Cost Histogram

The cost histogram shows the number of successful and failed instances per cost point.

Actions Histogram

The actions histogram shows per number of actions the number of successful and failed instances.