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-sweDeepSWE 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 |
|
Dataset ID |
|
Paper |
N/A |
Tags |
|
Metrics |
|
Default Shots |
0-shot |
Evaluation Split |
|
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 |
|---|---|---|---|
|
|
|
Optional list of DeepSWE task ids to evaluate. |
|
|
|
Optional task language filter from manifest metadata. |
|
|
|
Optional task category filter from manifest metadata. |
|
|
`` |
Optional deterministic shuffle seed applied before limit. |
|
|
|
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)