You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Based on a systematic review of **181 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.
35
+
Based on a systematic review of **183 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.
36
36
37
37
<!-- START EXPLORE -->
38
38
**🔍 Explore This Survey:**
@@ -67,7 +67,7 @@ Based on a systematic review of **181 papers and online resources**, this survey
67
67
## 📚 Complete Paper List
68
68
69
69
70
-
> **Total: 181 works** across 14 categories
70
+
> **Total: 183 works** across 14 categories
71
71
72
72
73
73
### 📊 Evaluation Datasets
@@ -292,6 +292,7 @@ Based on a systematic review of **181 papers and online resources**, this survey
-**SWE-Master**: SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training (2026) [](https://arxiv.org/abs/2602.03411)[](https://github.com/RUCAIBox/SWE-Master)
295
296
296
297
### ⚡ Inference-Time Scaling
297
298
@@ -325,6 +326,7 @@ Based on a systematic review of **181 papers and online resources**, this survey
325
326
-**SWE-smith**: SWE-smith: Scaling Data for Software Engineering Agents (2025) [](https://openreview.net/forum?id=63iVrXc8cC)
326
327
-**SWE-Flow**: Synthesizing Software Engineering Data in a Test-Driven Manner (2025) [](https://openreview.net/forum?id=P9DQ2IExgS)
327
328
-**SWE-Mirror**: SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories (2025) [](https://arxiv.org/abs/2509.08724)
329
+
-**SWE-World**: SWE-World: Building Software Engineering Agents in Docker-Free Environments (2026) [](https://arxiv.org/abs/2602.03419)[](https://github.com/RUCAIBox/SWE-World)
Copy file name to clipboardExpand all lines: docs/about.md
+1-1Lines changed: 1 addition & 1 deletion
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -2,7 +2,7 @@
2
2
3
3
## About This Project
4
4
5
-
Based on a systematic review of 181 papers and online resources, this project establishes a holistic theoretical framework for Issue Resolution in software engineering. This website is designed to facilitate efficient literature retrieval and exploration.
5
+
Based on a systematic review of 183 papers and online resources, this project establishes a holistic theoretical framework for Issue Resolution in software engineering. This website is designed to facilitate efficient literature retrieval and exploration.
Based on a systematic review of 181 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](https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution) and project documentation website.
60
+
Based on a systematic review of 183 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](https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution) and project documentation website.
61
61
62
62
**🔍 Explore This Survey:**
63
63
@@ -152,6 +152,7 @@ This section covers the datasets used for evaluation and training, as well as me
152
152
***SWE-smith**: SWE-smith: Scaling Data for Software Engineering Agents (2025) [](https://openreview.net/forum?id=63iVrXc8cC){: target="_blank" }
153
153
***SWE-Flow**: Synthesizing Software Engineering Data in a Test-Driven Manner (2025) [](https://openreview.net/forum?id=P9DQ2IExgS){: target="_blank" }
154
154
***SWE-Mirror**: SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories (2025) [](https://arxiv.org/abs/2509.08724){: target="_blank" }
155
+
***SWE-World**: SWE-World: Building Software Engineering Agents in Docker-Free Environments (2026) [](https://arxiv.org/abs/2602.03419){: target="_blank" } [](https://github.com/RUCAIBox/SWE-World){: target="_blank" }
155
156
<!-- END PAPERS:data_synthesis -->
156
157
157
158
---
@@ -345,6 +346,7 @@ This section covers both training-free and training-based methods for issue reso
<h2id="about-this-project">About This Project<aclass="headerlink" href="#about-this-project" title="Permanent link">¶</a></h2>
707
-
<p>Based on a systematic review of 181 papers and online resources, this project establishes a holistic theoretical framework for Issue Resolution in software engineering. This website is designed to facilitate efficient literature retrieval and exploration.</p>
707
+
<p>Based on a systematic review of 183 papers and online resources, this project establishes a holistic theoretical framework for Issue Resolution in software engineering. This website is designed to facilitate efficient literature retrieval and exploration.</p>
<p>Based on a systematic review of 181 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 <ahref="https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution">GitHub repository</a> and project documentation website. </p>
1052
+
<p>Based on a systematic review of 183 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 <ahref="https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution">GitHub repository</a> and project documentation website. </p>
1053
1053
<p><strong>🔍 Explore This Survey:</strong></p>
1054
1054
<ul>
1055
1055
<li>📊 <strong><ahref="#data">Data</a></strong>: Evaluation and training datasets, data collection and synthesis methods</li>
<li><strong>SWE-smith</strong>: SWE-smith: Scaling Data for Software Engineering Agents (2025) <ahref="https://openreview.net/forum?id=63iVrXc8cC" target="_blank"><imgalt="OpenReview" src="https://img.shields.io/badge/OpenReview-paper-8C1B13?logo=openreview&logoColor=white" /></a></li>
1141
1141
<li><strong>SWE-Flow</strong>: Synthesizing Software Engineering Data in a Test-Driven Manner (2025) <ahref="https://openreview.net/forum?id=P9DQ2IExgS" target="_blank"><imgalt="OpenReview" src="https://img.shields.io/badge/OpenReview-paper-8C1B13?logo=openreview&logoColor=white" /></a></li>
1142
1142
<li><strong>SWE-Mirror</strong>: SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories (2025) <ahref="https://arxiv.org/abs/2509.08724" target="_blank"><imgalt="arXiv" src="https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white" /></a></li>
1143
+
<li><strong>SWE-World</strong>: SWE-World: Building Software Engineering Agents in Docker-Free Environments (2026) <ahref="https://arxiv.org/abs/2602.03419" target="_blank"><imgalt="arXiv" src="https://img.shields.io/badge/arXiv-paper-B31B1B?logo=arxiv&logoColor=white" /></a><ahref="https://github.com/RUCAIBox/SWE-World" target="_blank"><imgalt="GitHub" src="https://img.shields.io/badge/GitHub-repo-24292F?logo=github&logoColor=white" /></a></li>
0 commit comments