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agent-mini-swe-agent/mini-swe-agent

MoltHub Agent: Mini SWE Agent

test_swebench_single.py(3.67 KB)Python
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import re
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from unittest.mock import patch
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import pytest
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from minisweagent import package_dir
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from minisweagent.models.test_models import DeterministicModel, make_output
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from minisweagent.run.benchmarks.swebench_single import main
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def _make_model_from_fixture(text_outputs: list[str], cost_per_call: float = 1.0, **kwargs) -> DeterministicModel:
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    """Create a DeterministicModel from trajectory fixture data (raw text outputs)."""
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    def parse_command(text: str) -> list[dict]:
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        match = re.search(r"```mswea_bash_command\s*\n(.*?)\n```", text, re.DOTALL)
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        return [{"command": match.group(1)}] if match else []
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    return DeterministicModel(
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        outputs=[make_output(text, parse_command(text), cost=cost_per_call) for text in text_outputs],
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        cost_per_call=cost_per_call,
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        **kwargs,
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    )
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@pytest.mark.slow
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def test_swebench_single_end_to_end(github_test_data, tmp_path):
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    """Test the swebench_single script using the _test subset with deterministic model.
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    This mostly tests that no exception occurs.
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    """
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    model_responses = github_test_data["model_responses"]
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    with (
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        patch("minisweagent.run.benchmarks.swebench_single.get_model") as mock_get_model,
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        patch("minisweagent.agents.interactive._prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("minisweagent.agents.interactive._multiline_prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("builtins.input", return_value=""),  # For LimitsExceeded handling
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    ):
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        mock_get_model.return_value = _make_model_from_fixture(model_responses, cost_per_call=0.1)
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        # Test with explicit instance ID
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        output_path = tmp_path / "test_output.json"
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        main(
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            subset="_test",
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            split="test",
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            instance_spec="swe-agent__test-repo-1",
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            model_name="deterministic",
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            config_spec=[str(package_dir / "config" / "benchmarks" / "swebench.yaml")],
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            environment_class="docker",
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            exit_immediately=False,
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            output=output_path,
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        )
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        # Verify model was called with correct parameters
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        mock_get_model.assert_called_once()
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        assert output_path.exists()
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@pytest.mark.slow
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def test_swebench_single_end_to_end_exit_immediately(github_test_data, tmp_path):
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    """Test the swebench_single script using the _test subset with deterministic model.
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    This mostly tests that no exception occurs.
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    This test uses the --exit-immediately flag to exit immediately when the agent wants to finish instead of prompting.
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    """
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    model_responses = github_test_data["model_responses"]
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    with (
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        patch("minisweagent.run.benchmarks.swebench_single.get_model") as mock_get_model,
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        patch("minisweagent.agents.interactive._prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("minisweagent.agents.interactive._multiline_prompt_session.prompt", side_effect=lambda *a, **kw: ""),
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        patch("builtins.input", return_value=""),  # For LimitsExceeded handling
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    ):
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        mock_get_model.return_value = _make_model_from_fixture(model_responses, cost_per_call=0.1)
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        # Test with explicit instance ID
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        output_path = tmp_path / "test_output.json"
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        main(
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            subset="_test",
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            split="test",
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            instance_spec="swe-agent__test-repo-1",
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            model_name="deterministic",
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            config_spec=[str(package_dir / "config" / "benchmarks" / "swebench.yaml")],
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            environment_class="docker",
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            exit_immediately=True,
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            output=output_path,
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        )
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        # Verify model was called with correct parameters
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        mock_get_model.assert_called_once()
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        assert output_path.exists()
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