evalstone/evalscope/docs/en/benchmarks/deep_swe.md

DeepSWE

Overview

DeepSWE is a coding-agent benchmark for evaluating repository-level software engineering tasks. EvalScope integrates it through Pier and runs each benchmark sample as one Pier Python API job.

Task Description

• Task Type: Agentic software engineering
• Input: DeepSWE task directory containing task metadata and verifier assets
• Output: A repository patch produced by a Pier built-in agent
• Scoring: Binary verifier reward exposed as acc

Evaluation Notes

• Requires Python>=3.12, Docker, and pip install evalscope[deep_swe]
• Dataset defaults to ModelScope evalscope/deep-swe
• DeepSWE runs through Pier's Docker environment in EvalScope
• Use pier_agent_kwargs={'model_class': 'litellm'} for OpenAI-compatible providers that do not support Responses API

Properties

Property	Value
Benchmark Name	deep_swe
Dataset ID	evalscope/deep-swe
Paper	N/A
Tags	Agent, Coding, MultiTurn
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	113
Prompt Length (Mean)	2158.07 chars
Prompt Length (Min/Max)	471 / 5385 chars

Sample Example

Subset: test

{
  "input": [
    {
      "id": "f61040e0",
      "content": "Add a new `errorStack` constructor option to SuperJSON. Omitting it leaves existing Error behavior unchanged.\n\nThe option shape is `{ mode?, normalizeNewlines?, trimLeadingWhitespace?, maxStackLines?, stripInternalFrames?, redactPaths?, inclu ... [TRUNCATED 3577 chars] ... ): Processor | undefined`. `normalizeErrorStackOptions` returns `undefined` for any non-object input (`null`, `undefined`, strings).\n\nBefore writing, read through the existing error serialization logic and the `allowedErrorProps` mechanism.\n\n"
    }
  ],
  "target": "",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "ext_id": "kh701jywhzgddknqwzsq6npjv98226tq",
    "task_id": "superjson-error-stack-serialization",
    "display_title": "Add error stack serialization to SuperJSON",
    "display_description": "Add configurable serialization and restoration of error stacks, stack frames, causes, and sanitization in SuperJSON.",
    "repo": "flightcontrolhq/superjson",
    "repository_url": "https://github.com/flightcontrolhq/superjson.git",
    "original_title": "Error Stack Serialization Support",
    "category": "feature_request",
    "language": "typescript",
    "task_path": "~/.cache/evalscope/deep_swe/snapshots/evalscope/deep-swe/tasks/superjson-error-stack-serialization",
    "task_toml_path": "~/.cache/evalscope/deep_swe/snapshots/evalscope/deep-swe/tasks/superjson-error-stack-serialization/task.toml",
    "instruction": "Add a new `errorStack` constructor option to SuperJSON. Omitting it leaves existing Error behavior unchanged.\n\nThe option shape is `{ mode?, normalizeNewlines?, trimLeadingWhitespace?, maxStackLines?, stripInternalFrames?, redactPaths?, inclu ... [TRUNCATED 3577 chars] ... ): Processor | undefined`. `normalizeErrorStackOptions` returns `undefined` for any non-object input (`null`, `undefined`, strings).\n\nBefore writing, read through the existing error serialization logic and the `allowedErrorProps` mechanism.\n\n"
  }
}

Prompt Template

Prompt Template:

{question}

Extra Parameters

Parameter	Type	Default	Description
task_ids	list	[]	Optional list of DeepSWE task ids to evaluate.
languages	list	[]	Optional task language filter from manifest metadata.
categories	list	[]	Optional task category filter from manifest metadata.
sample_seed	int	``	Optional deterministic shuffle seed applied before limit.
pier_agent_kwargs	dict	{}	Extra kwargs passed to Pier AgentConfig.kwargs.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets deep_swe \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['deep_swe'],
    dataset_args={
        'deep_swe': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)