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‎README.md‎

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[![Hugging Face](https://img.shields.io/badge/HF_Paper-2601.11655-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/papers/2601.11655)
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[![Tables](https://img.shields.io/badge/TABLES-Statistics-blue?style=for-the-badge&logo=databricks)](https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/tables/)
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[![Contributors](https://img.shields.io/github/contributors/DeepSoftwareAnalytics/Awesome-Issue-Resolution?style=for-the-badge&color=green&logo=github)](https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/graphs/contributors)
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![Papers Count](https://img.shields.io/badge/papers-197-green?style=for-the-badge&logo=googlescholar&logoColor=white)
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![Papers Count](https://img.shields.io/badge/papers-198-green?style=for-the-badge&logo=googlescholar&logoColor=white)
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[**📖 Documentation Website**](https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/) | [**📄 Full Paper**](https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/paper/) | [**📋 Tables & Resources**](https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/tables/)
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@@ -32,21 +32,32 @@
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## 📖 Abstract
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Based on a systematic review of **197 papers and online resources**, this survey establishes a holistic theoretical framework for Issue Resolution in software engineering. We examine how **Large Language Models (LLMs)** are transforming the automation of GitHub issue resolution. Beyond the theoretical analysis, we have curated a comprehensive collection of datasets and model training resources, which are continuously synchronized with our GitHub repository and project documentation website.
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Based on a systematic review of **198 papers and online resources**, this survey establishes a holistic theoretical framework for Issue Resolution in software engineering. We examine how **Large Language Models (LLMs)** are transforming the automation of GitHub issue resolution. Beyond the theoretical analysis, we have curated a comprehensive collection of datasets and model training resources, which are continuously synchronized with our GitHub repository and project documentation website.
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> 🔎 **Browse & Export**: The full paper database is searchable and exportable at **[deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/admin/](https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/admin/)** — filter by category, date, or keyword, and export results as CSV.
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## 📰 News
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<!-- START NEWS -->
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### This Month's Papers
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<!-- START_THIS_MONTH -->
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**2 paper(s) — 2026-03**
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### Recent Papers
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<!-- START_RECENT_PAPERS -->
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- **BeyondSWE**: BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing? [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.03194)
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- **SWE-Adept**: SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.01327)
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<!-- END_THIS_MONTH -->
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- **Closing the Loop**: Closing the Loop: Universal Repository Representation with RPG-Encoder [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.02084)
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- **Rust-SWE-bench**: Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.22764)
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- **Scale-SWE**: Immersion in the GitHub Universe: Scaling Coding Agents to Mastery [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.09892)
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- **SWE Context Bench**: SWE Context Bench: A Benchmark for Context Learning in Coding [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/pdf/2602.08316)
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- **SWE-ABS**: SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.00520)
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- **SWE-Bench Mobile**: SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level Mobile Applications? [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.09540)
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- **SWE-Hub**: SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.00575)
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- **SWE-Master**: SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.03411)
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- **SWE-MiniSandbox**: SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.11210v1)
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- **SWE-Protégé**: SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.22124)
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- **SWE-Universe**: SWE-Universe: Scale Real-World Verifiable Environments to Millions [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://www.arxiv.org/abs/2602.02361)
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- **SWE-World**: SWE-World: Building Software Engineering Agents in Docker-Free Environments [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.03419)
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- **SWE-rebench V2**: SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.23866)
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<!-- END_RECENT_PAPERS -->
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### Recent Updates
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## 📚 Complete Paper List
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> **Total: 197 works** across 14 categories
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> **Total: 198 works** across 14 categories
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### 📊 Evaluation Datasets
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- `(2026-02)` **SWE Context Bench**: SWE Context Bench: A Benchmark for Context Learning in Coding [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/pdf/2602.08316)
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- `(2026-02)` **SWE-ABS**: SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.00520)
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- `(2026-02)` **Rust-SWE-bench**: Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.22764) [![GitHub](https://img.shields.io/badge/GitHub-repo-24292F?logo=github&logoColor=white)](https://github.com/GhabiX/Rust-SWE-Bench)
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- `(2026-02)` **SWE-Bench Mobile**: SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level Mobile Applications? [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.09540) [![Website](https://img.shields.io/badge/Website-paper-5B9BD5?logo=googlechrome&logoColor=white)](https://swebenchmobile.com/)
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- `(2025-12)` **SWE-InfraBench**: SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code [![OpenReview](https://img.shields.io/badge/OpenReview-paper-8C1B13?logo=openreview&logoColor=white)](https://openreview.net/forum?id=XX0ciUwfXa)
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- `(2025-12)` **SWE-EVO**: SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2512.18470)
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- `(2025-11)` **SWE-Sharp-Bench**: SWE-Sharp-Bench: A Reproducible Benchmark for C# Software Engineering Tasks [![arXiv](https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2511.02352)
@@ -556,7 +568,7 @@ Open **http://localhost:5000/admin** to manage papers, datasets, and methods.
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| `python start.py --init` | Re-import from YAML/CSV, then full update + start |
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| `python start.py --no-update` | Start server without running update steps |
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| `python start.py --port 8080` | Use a custom port |
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| `python start.py --news` | Refresh This Month's Papers only and exit |
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| `python start.py --news` | Refresh Recent Papers section only and exit |
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| `python start.py --render` | Re-render README/docs from DB only and exit |
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| `python start.py --build` | Build static site (mkdocs) only and exit |
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‎admin/static/js/admin.js‎

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@@ -648,7 +648,8 @@ function renderPapersTable(items) {
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<input type="checkbox" class="paper-checkbox" id="select-all-cb"
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title="Select all" ${allChecked ? 'checked' : ''}
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onchange="toggleSelectAll(this)">
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</th>`;
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</th>
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<th title="Pin to Recent Papers section">Featured</th>`;
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sortableColumns.forEach(col => {
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if (col.sortable) {
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const displayMonth = paper.month ? paper.month.replace('-', '.') : '';
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const monthValue = paper.month || '';
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const checked = selectedPapers.has(paper.id) ? 'checked' : '';
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const featuredClass = paper.featured ? 'featured-star active' : 'featured-star';
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const featuredTitle = paper.featured ? 'Remove from Recent Papers' : 'Pin to Recent Papers';
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html += `
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<tr data-id="${paper.id}" data-type="papers" class="${selectedPapers.has(paper.id) ? 'row-selected' : ''}">
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<tr data-id="${paper.id}" data-type="papers" class="${selectedPapers.has(paper.id) ? 'row-selected' : ''}${paper.featured ? ' row-featured' : ''}">
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<td class="cb-col">
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<input type="checkbox" class="paper-checkbox" data-id="${paper.id}"
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${checked} onchange="togglePaperSelection(this, ${paper.id})">
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</td>
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<td class="featured-col">
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<button class="${featuredClass}" data-id="${paper.id}"
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title="${featuredTitle}" onclick="toggleFeatured(${paper.id}, this)">★</button>
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</td>
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<td><span class="badge badge-primary">${paper.short_name}</span></td>
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<td class="editable-cell truncate" data-field="title" data-value="${escapeHtml(paper.title)}" title="${paper.title}">${paper.title}</td>
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<td class="editable-cell truncate" data-field="authors" data-value="${escapeHtml(paper.authors)}" title="${paper.authors}">${paper.authors}</td>
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return html;
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}
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// Toggle featured status for a paper
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function toggleFeatured(paperId, btn) {
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fetch(`${API_BASE}/papers/${paperId}/toggle-featured`, { method: 'POST' })
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.then(r => r.json())
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.then(data => {
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if (data.error) { showToast(data.error, 'error'); return; }
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const row = btn.closest('tr');
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if (data.featured) {
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btn.classList.add('active');
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btn.title = 'Remove from Recent Papers';
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row.classList.add('row-featured');
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} else {
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btn.classList.remove('active');
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btn.title = 'Pin to Recent Papers';
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row.classList.remove('row-featured');
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}
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// Update local data cache
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const paper = currentData.find(p => p.id === paperId);
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if (paper) paper.featured = data.featured;
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showToast(data.featured ? '★ Added to Recent Papers' : '☆ Removed from Recent Papers', 'success');
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})
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.catch(() => showToast('Failed to update featured status', 'error'));
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}
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function attachColumnSortListeners(tableType) {
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‎admin/templates/papers.html‎

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.paper-checkbox { width: 16px; height: 16px; cursor: pointer; accent-color: #667eea; }
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tr.row-selected { background: #f0f0ff !important; }
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/* Featured star column */
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.featured-col { width: 52px; text-align: center; padding: 0 4px !important; }
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.featured-star {
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background: none;
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border: none;
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font-size: 18px;
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cursor: pointer;
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color: #ccc;
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line-height: 1;
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padding: 2px 4px;
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border-radius: 4px;
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transition: color 0.2s, transform 0.15s;
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}
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.featured-star:hover { color: #f5a623; transform: scale(1.2); }
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.featured-star.active { color: #f5a623; }
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tr.row-featured { background: #fffbf0 !important; }
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.badge-primary {
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color: #1976d2;

‎controllers/api_controller.py‎

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return jsonify({'error': str(e)}), 400
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@api.route('/papers/<int:paper_id>/toggle-featured', methods=['POST'])
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def toggle_featured(paper_id):
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"""Toggle the featured flag on a paper"""
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paper = session.query(Paper).get(paper_id)
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if not paper:
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return jsonify({'error': 'Paper not found'}), 404
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try:
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paper.featured = not bool(paper.featured)
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session.commit()
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return jsonify({'id': paper.id, 'featured': bool(paper.featured)})
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except Exception as e:
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session.rollback()
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return jsonify({'error': str(e)}), 400
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@api.route('/papers/<int:paper_id>', methods=['DELETE'])
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def delete_paper(paper_id):
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"""Delete a paper"""

‎data/papers_evaluation_datasets.yaml‎

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links:
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arxiv: https://arxiv.org/abs/2602.22764
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github: https://github.com/GhabiX/Rust-SWE-Bench
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- short_name: SWE-Bench Mobile
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title: 'SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level
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Mobile Applications?'
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authors: Muxin Tian, Zhe Wang, Blair Yang, Zhenwei Tang, Kunlun Zhu, Honghua Dong,
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Hanchen Li, Xinni Xie, Guangjing Wang, Jiaxuan You
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year: '2026'
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venue: arXiv preprint arXiv:2602.09540
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month: 2026-02
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links:
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arxiv: https://arxiv.org/abs/2602.09540
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website: https://swebenchmobile.com/
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- short_name: SWE-InfraBench
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title: 'SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code'
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authors: Natalia Tarasova, Enrique Balp-Straffon, Aleksei Iancheruk, Yevhenii Sielskyi,

‎database/migrate.py‎

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print(f"[OK] Migrated {count} foundation models")
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def add_missing_columns():
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"""Add any columns that exist in the model but are missing from the DB (safe to re-run)."""
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from models import get_engine
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import sqlalchemy as sa
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engine = get_engine()
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with engine.connect() as conn:
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inspector = sa.inspect(engine)
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existing = {col['name'] for col in inspector.get_columns('papers')}
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if 'featured' not in existing:
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conn.execute(sa.text('ALTER TABLE papers ADD COLUMN featured BOOLEAN DEFAULT 0'))
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conn.commit()
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print("[OK] Added 'featured' column to papers table")
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def main():
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print("\n" + "="*70)
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print(" Data Migration: CSV/YAML → SQLite")
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# Initialize database
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print("[INFO] Initializing database...")
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init_db()
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# Add any new columns to existing tables
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print("[INFO] Checking for schema updates...")
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add_missing_columns()
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# Migrate data
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print("\n[INFO] Migrating papers from YAML files...")

‎docs/admin/data.json‎

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{
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"generated": "2026-03-05",
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"stats": {
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"total": 197,
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"data": 49,
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"total": 198,
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"data": 50,
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"methods": 128,
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"analysis": 21
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},
@@ -34,6 +34,19 @@
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"arxiv": "https://arxiv.org/abs/2603.01327"
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}
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},
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{
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"id": 199,
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"short_name": "SWE-Bench Mobile",
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"title": "SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level Mobile Applications?",
41+
"authors": "Muxin Tian, Zhe Wang, Blair Yang, Zhenwei Tang, Kunlun Zhu, Honghua Dong, Hanchen Li, Xinni Xie, Guangjing Wang, Jiaxuan You",
42+
"venue": "arXiv preprint arXiv:2602.09540",
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"month": "2026-02",
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"category": "evaluation_datasets",
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"links": {
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"arxiv": "https://arxiv.org/abs/2602.09540",
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"website": "https://swebenchmobile.com/"
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}
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},
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{
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"id": 198,
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"short_name": "Rust-SWE-bench",

‎docs/admin/index.md‎

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@@ -40,7 +40,7 @@ Then open **[http://localhost:5000/admin](http://localhost:5000/admin)** in your
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| `python start.py` | Full update + start server (port 5000) |
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| `python start.py --init` | Re-import from YAML/CSV, then full update + start |
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| `python start.py --no-update` | Start server immediately without update steps |
43-
| `python start.py --news` | Refresh "This Month's Papers" only |
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| `python start.py --news` | Refresh "Recent Papers" section only |
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| `python start.py --render` | Re-render README/docs from DB only |
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| `python start.py --build` | Build static site only |
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‎docs/index.md‎

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<a href="https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/graphs/contributors" target="_blank">
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<img src="https://img.shields.io/github/contributors/DeepSoftwareAnalytics/Awesome-Issue-Resolution?style=for-the-badge&color=green&logo=github" alt="Contributors">
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</a>
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<img src="https://img.shields.io/badge/papers-197-green?style=for-the-badge&logo=googlescholar&logoColor=white" alt="Papers Count">
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<img src="https://img.shields.io/badge/papers-198-green?style=for-the-badge&logo=googlescholar&logoColor=white" alt="Papers Count">
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</div>
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<!-- Interactive Exploration Badges -->

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