| --- |
| dataset_info: |
| config_name: default |
| splits: |
| - name: test |
| num_examples: 124 |
| --- |
| |
| # SWE-Atlas QnA |
|
|
| Codebase QnA is the first benchmark in the SWE-Atlas suite. It evaluates AI agents on deep code comprehension — tracing execution paths, explaining architectural decisions, and answering deeply technical questions about production-grade software systems. |
|
|
| 124 tasks across 11 open-source repositories spanning Go, Python, C, and TypeScript. |
|
|
| Link to leaderboard - [https://scale.com/leaderboard/sweatlas-qna](https://scale.com/leaderboard/sweatlas-qna) |
|
|
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `task_id` | string | Unique 24-char hex identifier | |
| | `prompt` | string | The question presented to the agent | |
| | `reference_answer` | string | Expert-written reference answer | |
| | `repository_url` | string | GitHub repo | |
| | `repository_base_commit` | string | 40-char commit SHA the environment is pinned to | |
| | `language` | string | `go`, `python`, `c`, or `ts` | |
| | `category` | string | Task category (see below) | |
| | `rubric` | string (JSON) | Evaluation criteria (see below) | |
| | `docker_image` | string | Docker Hub image for the sandboxed environment | |
|
|
| ### Rubric format |
|
|
| Each task's `rubric` field is a JSON array: |
|
|
| ```json |
| [ |
| { |
| "id": "a33fc01cba19849aaf3b55e6b801001c", |
| "title": "1.1: States that kitty uses Unix sockets for external connections...", |
| "annotations": { |
| "type": "positive hli verifier", |
| "importance": "must have" |
| } |
| } |
| ] |
| ``` |
|
|
| - `positive hli verifier` — a factual claim the answer must contain. If the claim is met my the agent's answer, the rubric item result is a PASS. |
| - `negative hli verifier` — something the answer must *not* claim. If the claim is met my the agent's answer, the rubric item result is a FAIL. |
|
|
|
|
| Each task includes a `docker_image` field pointing to a pre-built Docker Hub image with the repository and all dependencies installed at `/app`: |
|
|
|
|
| ## Inference and Eval |
|
|
| We follow the standard SWE-Agent scaffold, and we provide a sample config (with the prompts) in [default_qa_config.yaml](default_qa_config.yaml) |
|
|
| To run tasks, you can pull the docker image and run the container, and reset the environment to the base commit: |
|
|
| ```bash |
| cd /app |
| git config --global --add safe.directory /app |
| git restore . |
| git reset --hard <repository_base_commit> |
| git clean -fdq |
| ``` |
|
|
| Evaluation is performed by an LLM judge (Claude Opus 4.5) that scores the agent's answer against each rubric criterion independently. Each criterion receives a binary score (met or not met) indicating and is then aggregated. |
|
|
| The primary metric is the Task Resolve Rate: the percentage of tasks for which the agent's answer is comprehensive (i.e. passes all rubric items and scores 1.0), as graded by a set of task-specific rubrics. |
|
|
| The agents are also instructed to avoid modifying source-code files, and clean up any temporary scripts made. So we add a programmatic check that fails a task that has any code changes after submission. |
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|
| Our rubric evaluation prompt and other relevant details are in [rubric_evaluation_config.yaml](rubric_evaluation_config.yaml) |
