13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
126 lines
4.7 KiB
Markdown
126 lines
4.7 KiB
Markdown
# DeepSWE
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## 概述
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DeepSWE 是一个用于评估仓库级软件工程任务的编码智能体基准测试。EvalScope 通过 Pier 集成该基准，并将每个基准样本作为一项 Pier Python API 任务运行。
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## 任务描述
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- **任务类型**：智能体软件工程
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- **输入**：包含任务元数据和验证器资源的 DeepSWE 任务目录
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- **输出**：由 Pier 内置智能体生成的代码仓库补丁
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- **评分方式**：二值验证器奖励，以 `acc` 形式暴露
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## 评估说明
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- 要求 **Python>=3.12**、Docker，以及执行 `pip install evalscope[deep_swe]`
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- 数据集默认使用 ModelScope 上的 `evalscope/deep-swe`
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- DeepSWE 在 EvalScope 中通过 Pier 的 Docker 环境运行
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- 对于不支持 Responses API 的 OpenAI 兼容提供商，请使用 `pier_agent_kwargs={'model_class': 'litellm'}`
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `deep_swe` |
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| **数据集ID** | [evalscope/deep-swe](https://modelscope.cn/datasets/evalscope/deep-swe/summary) |
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| **论文** | 无 |
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| **标签** | `Agent`, `Coding`, `MultiTurn` |
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| **指标** | `accuracy` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `test` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 113 |
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| 提示词长度（平均） | 2158.07 字符 |
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| 提示词长度（最小/最大） | 471 / 5385 字符 |
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## 样例示例
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**子集**: `test`
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```json
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{
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"input": [
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{
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"id": "f61040e0",
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"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"
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}
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],
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"target": "",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"ext_id": "kh701jywhzgddknqwzsq6npjv98226tq",
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"task_id": "superjson-error-stack-serialization",
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"display_title": "Add error stack serialization to SuperJSON",
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"display_description": "Add configurable serialization and restoration of error stacks, stack frames, causes, and sanitization in SuperJSON.",
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"repo": "flightcontrolhq/superjson",
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"repository_url": "https://github.com/flightcontrolhq/superjson.git",
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"original_title": "Error Stack Serialization Support",
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"category": "feature_request",
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"language": "typescript",
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"task_path": "~/.cache/evalscope/deep_swe/snapshots/evalscope/deep-swe/tasks/superjson-error-stack-serialization",
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"task_toml_path": "~/.cache/evalscope/deep_swe/snapshots/evalscope/deep-swe/tasks/superjson-error-stack-serialization/task.toml",
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"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"
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}
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}
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```
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## 提示模板
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**提示模板:**
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```text
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{question}
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```
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## 额外参数
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| 参数 | 类型 | 默认值 | 描述 |
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|-----------|------|---------|-------------|
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| `task_ids` | `list` | `[]` | 可选的 DeepSWE 任务 ID 列表，用于指定评估范围。 |
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| `languages` | `list` | `[]` | 可选的任务语言过滤器，基于清单元数据。 |
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| `categories` | `list` | `[]` | 可选的任务类别过滤器，基于清单元数据。 |
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| `sample_seed` | `int` | `` | 可选的确定性打乱种子，在限制样本数量前应用。 |
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| `pier_agent_kwargs` | `dict` | `{}` | 传递给 Pier AgentConfig.kwargs 的额外关键字参数。 |
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## 使用方法
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### 使用 CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets deep_swe \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['deep_swe'],
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dataset_args={
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'deep_swe': {
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# extra_params: {} # 使用默认额外参数
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}
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},
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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```
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