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+assets/files_edited_distr.png filter=lfs diff=lfs merge=lfs -text
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+assets/license_distr.png filter=lfs diff=lfs merge=lfs -text
+assets/loc_edited_distr.png filter=lfs diff=lfs merge=lfs -text
+assets/prog_language_distr.png filter=lfs diff=lfs merge=lfs -text
+assets/repo_type_distr.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+pretty_name: SWE-bench Atlas
+tags:
+- software-engineering
+- benchmark
+- evaluation
+- LLM
+- reasoning
+- SWE-bench
+- docker
+size_categories:
+- 1K<n<10K
+dataset_type: dataset
+multilinguality: multilingual
+dataset_info:
+  features:
+  - name: repo
+    dtype: string
+    description: Repository name where the issue or PR originated.
+  - name: instance_id
+    dtype: string
+    description: Unique identifier for the SWE-bench++ instance.
+  - name: language
+    dtype: string
+    description: Programming language of the repository.
+  - name: difficulty
+    dtype: string
+    description: Difficulty level assigned to the instance (e.g., easy, medium, hard).
+  - name: task_type
+    dtype: string
+    description: Task category such as bug fixing, code repair, or reasoning.
+  - name: repo_type
+    dtype: string
+    description: Repository type or domain classification (e.g., web, ML, library).
+  - name: base_commit
+    dtype: string
+    description: Git commit hash representing the base state before the patch.
+  - name: patch
+    dtype: string
+    description: The unified diff or patch applied to fix the issue.
+  - name: test_patch
+    dtype: string
+    description: Patch applied specifically to modify or add test cases.
+  - name: problem_statement
+    dtype: string
+    description: Human-readable summary of the issue or problem being solved.
+  - name: created_at
+    dtype: string
+    description: Date when the instance or pull request was created.
+  - name: FAIL_TO_PASS
+    dtype: string
+    description: Output or diff representing the transition from failing to passing tests.
+  - name: PASS_TO_PASS
+    dtype: string
+    description: Output or diff for test cases that passed before and after the patch.
+  - name: environment_config
+    dtype: string
+    description: environment_config used to setup and reproduce the issue environment.
+  splits:
+  - name: test
+    num_bytes: 33891350
+    num_examples: 500
+  download_size: 5548449
+  dataset_size: 33891350
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test.csv
+---
+
+# SWE-bench Atlas
+
+## 1. Summary
+
+In the domain of software engineering, LLM capabilities have progressed rapidly, underscoring the need for evolving evaluation frameworks. While foundational, benchmarks like SWE-bench, SWE-bench Verified, and other such variants are incomplete, with manually curated design causing scalability bottlenecks, weak test oracles, dataset aging and contamination, reproducibility challenges, and more.
+
+In response, we introduce **SWE-bench Atlas**: a reenvisioned, innovative, end-to-end evaluation framework. It both addresses existing evaluation pain points and introduces new capabilities, positioning it as a forerunner for software reasoning evaluation and training. Our initial private validation benchmark consists of **7,000+ GitHub instances** from **1000s of repositories** across **9 languages**.
+
+We’ve made 500 of these instances publicly available, with over 80% being in the medium-to-hard difficulty range. These tasks average **120+ lines of code edited** (with a considerable number in the 1000s+ range) and **7+ files edited**. Highest performing model scores are: **gpt-5-2025-08-07 at 26.8%, claude-sonnet-4.5 at 26%, gpt5-high-reasoning at 22.7%, and claude-opus-4.1 at 22.5%**, with a drop to **<14%** for the next highest performing models. See below for more details.
+
+SWE-bench++ is far more extensive than many previously released benchmarks, as its design enables automation of nearly the entire pipeline for its creation. This pipeline is unique because of its facilitation of scale and its ability to be generalized (especially to evaluation of other, more holistic software engineering tasks).
+
+---
+
+## 2. Getting Started
+
+### Evaluating Models on SWE-bench++
+
+To evaluate your model's performance on this dataset, visit our [evaluation repository](https://github.com/swebenchatlasanon-code/swe-bench-atlas-anon).
+
+This repository provides:
+- Complete evaluation harness with Docker-based testing environments
+- Step-by-step setup instructions
+- Example prediction file formats
+- Comprehensive troubleshooting guides
+
+### Quick Start
+
+```bash
+# Install the evaluation framework
+git clone https://github.com/swebenchatlasanon-code/swe-bench-atlas-anon
+cd SWE-Bench-plus-plus/SWE-Bench
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -e .
+
+# Run evaluation using this Hugging Face dataset
+python -m swebench.harness.run_evaluation \
+    --dataset_name swebenchatlas/swe-bench-atlas-public \
+    --predictions_path <path/to/your/predictions.jsonl> \
+    --namespace "" \
+    --run_id <run_id> \
+    --atlas_eval
+```
+
+For detailed instructions, please refer to the [Evaluation Guide](https://github.com/swebenchatlasanon-code/swe-bench-atlas-anon) in the repository.
+
+---
+
+## 3. Benchmark Construction (Methodology)
+
+We follow the framework below in our benchmark construction and evaluation pipeline.
+
+<p align="center">
+  <img src="assets/swe_framework.png" alt="SWE-bench++ Framework" width="700"/>
+  <i>Figure 1: SWE-bench++ Framework</i>
+</p>
+
+SWE-bench++ introduces **6 key innovations** that enable this:
+
+1. **Scalable sourcing and filtering (capture tasks):**  
+   We use heuristics to broadly select pull requests (PRs) that match our quality thresholds — active maintenance with recent commit activity, >100 stars + a recognizable testing framework, up to 10k lines of code changes, and merged PRs that explicitly close an issue.
+
+2. **Intelligent data curation (refine tasks):**  
+   We combine agent verification with human-expert verification to ensure high-quality PRs (problems that are specific enough, hard enough, and able to be containerized).
+
+3. **Agentic Dockerization (Dockerize tasks):**  
+   We combine two strategies to package each PR: template-based scaffolding and LLM-based containerizing (we generate Dockerfile templates for each programming language and use an agent to intelligently fill in blanks).
+
+4. **LLM-powered quality control (validate tasks):**  
+   We employ an agent once more as the final data validation step to check for issues that may slip through in a successful Docker build (e.g., redundant steps, inaccurate test commands, etc.).
+
+5. **Diagnostic feedback (analyze failures):**  
+   We eliminate manual engineering and debugging by using 3 states to analyze test outcomes — base, before, and after — as well as hybrid log parsing to extract test results from execution logs.  
+   Our hybrid log parser employs both a standard parser and an LLM-generated one to enable model debugging with unstructured test outputs.
+
+6. **Automated trajectory curation for fine-tuning (turn insights into training data):**  
+   We curate agentic trajectories as the model reasons through tasks in our dataset. These trajectories serve as valuable demonstrations for fine-tuning and enable hill climbing of other SWE benchmarks.
+
+---
+
+## 4. Results
+
+To validate the complexity of this new dataset, we benchmarked SOTA LLM agents using **swe-agent** on 500 instances and measured **pass@1**. The wide performance gap, with pass@1 scores ranging from **26.8% down to 1%**, confirms both that the dataset is challenging and that there is a clear model hierarchy.
+
+<p align="center">
+  <img src="assets/resolve_rate.png" alt="SWE-bench++ Evaluation Results: Resolve Rate by Model" width="850"/>
+  <i>Figure 2: SWE-bench++ Evaluation Results (Resolve Rate by Model)</i>
+</p>
+
+
+---
+
+## 5. Metadata
+
+### Overview
+
+**SWE-bench++ (Public)** is the community-accessible release of our extended SWE-bench benchmark. It includes 500 high-quality tasks designed to evaluate the ability of LLMs and coding agents to resolve real-world GitHub issues and pull requests. This dataset prioritizes both quantity and quality of tasks, having captured, scraped, and packaged diverse, difficult, high-quality PRs.
+
+### Key Features
+
+- **Task Scale:** 500 tasks across diverse repos and languages.
+- **Multilinguality:** 7 programming languages
+- **Repository Coverage:** 11 repo types
+- **Issue Coverage:** 6 issue types
+- **No Copyright Issues**
+
+We outline these distributions below.
+
+---
+
+<p align="center">
+  <img src="assets/prog_language_distr.png" alt="Task Distribution of Coding Languages" width="700"/>
+  <i>Figure 3: SWE-bench++ Task Distribution of Coding Languages</i>
+</p>
+
+<br>
+
+<p align="center">
+  <img src="assets/issue_type_distr.png" alt="Issue Type Distribution Across SWE-bench++ Tasks" width="700"/>
+  <i>Figure 4: Issue Type Distribution Across SWE-bench++ Tasks</i>
+</p>
+
+<br>
+
+<p align="center">
+  <img src="assets/repo_type_distr.png" alt="Repository Type Distribution" width="700"/>
+  <i>Figure 5: Repository Type Distribution</i>
+</p>
+
+Our heuristic-based sourcing step, which is intentionally coarse and fast, enables us to collect a high quantity of PRs (our initial run collected over 50,000). This size allows us to retain a high repository coverage, even as we prune for quality.
+
+<br>
+
+<p align="center">
+  <img src="assets/difficulty_distr.png" alt="Difficulty Level Distribution" width="700"/>
+  <i>Figure 6: Task Difficulty Level Distribution</i>
+</p>
+
+We categorize difficulty level based on the number of lines of code edited and the number of files edited [placeholder, waiting for official numbers]:
+
+```
+if # lines of code edited > [x1] and # files edited > [x2]:
+	task = hard
+if # lines of code edited > [y1] and # files edited > [y2]:
+	task = medium
+if # lines of code edited > [z1] and # files edited > [z2]:
+	task = easy
+```
+
+This distribution demonstrates the overall difficulty of this dataset, with over 80% of tasks being medium or above difficulty.
+
+See more metadata, including lines of code edited, files edited, and license counts in the appendix.
+
+---
+
+## 6. Implications and Conclusion
+
+The path to ASI resembles a three-legged race between model improvement and human evaluation: models get better, benchmarks adjust, and the cycle repeats. Essentially, models can only be systematically improved when benchmarks are rigorous enough to surface their limitations, creating a feedback loop where better models demand better benchmarks, and vice versa. Each side is dependent on the other to push forward.
+
+On the "benchmark side," SWE-bench++ gives the push ahead needed to stabilize the team.
+
+This framework both generalizes to other software engineering tasks (including those that may have non-standard build procedures or dependencies on external hardware) and paves the way for model hill-climbing and future research advancements (e.g., realistic, evolving RL gyms).
+
+SWE-bench++ sets a new standard for evaluating and training software reasoning capabilities, with its core innovations addressing leaderboard overfitting and enabling the development of models that can more robustly **reason**, **self-correct**, and **plan**.
+
+---
+
+## 7. Appendix
+
+We include more task metadata below, emphasizing this dataset’s wide coverage.
+
+<p align="center">
+  <img src="assets/loc_edited_distr.png" alt="Lines of Code Edited Distribution" width="700"/>
+  <i>Figure 7: Lines of Code Edited Distribution (From PR)</i>
+</p>
+
+
+<p align="center">
+  <img src="assets/files_edited_distr.png" alt="Number of Files Edited Distribution" width="700"/>
+  <i>Figure 8: Number of Files Edited (From PR) Distribution</i>
+</p>
+
+
+<p align="center">
+  <img src="assets/license_distr.png" alt="Licenses" width="700"/>
+  <i>Figure 9: Count of Licenses (From Repo)</i>
+</p>
+
+
+---
+
+**SWE-bench++ Overview**
+
+1. Summary  
+2. Getting Started
+3. Benchmark Construction (Methodology)  
+4. Results  
+5. Metadata  
+6. Implications and Conclusion  
+7. Licensing and Permissions  
+8. Appendix

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