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15 | 15 |
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16 | 16 | [**📖 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/) |
17 | 17 |
|
| 18 | +**🎙️ Interactive Exploration:** |
| 19 | + |
| 20 | +[](https://notebooklm.google.com/notebook/2b70100e-9d5a-46db-96f5-6ccb7b53890a) |
| 21 | +[](https://discord.gg/3nF2EYTD) |
| 22 | +[](https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/issues) |
| 23 | + |
18 | 24 | <img src="docs/images/awesome-issue-resolution.png" alt="Awesome Issue Resolution" width="60%"> |
19 | 25 |
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20 | 26 | </div> |
@@ -52,16 +58,6 @@ Based on a systematic review of **176 papers and online resources**, this survey |
52 | 58 | - 🤝 **[Contributing](#-contributing)**: How to contribute to this project |
53 | 59 | <!-- END EXPLORE --> |
54 | 60 |
|
55 | | -**🎙️ Interactive Exploration:** |
56 | | - |
57 | | -<div align="center"> |
58 | | - |
59 | | -[](https://notebooklm.google.com/notebook/2b70100e-9d5a-46db-96f5-6ccb7b53890a) |
60 | | -[](https://discord.gg/3nF2EYTD) |
61 | | -[](https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution/issues) |
62 | | - |
63 | | -</div> |
64 | | - |
65 | 61 | --- |
66 | 62 |
|
67 | 63 | <!-- START PAPERS --> |
@@ -102,7 +98,7 @@ Based on a systematic review of **176 papers and online resources**, this survey |
102 | 98 |
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103 | 99 | ### 🎯 Training Datasets |
104 | 100 |
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105 | | -*Datasets for training issue resolution agents* |
| 101 | +*Datasets for training issue resolution systems* |
106 | 102 |
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107 | 103 | - **SWE-bench-extra**: SWE-bench: Can Language Models Resolve Real-world Github Issues? (2024) |
108 | 104 | - **Multi-SWE-RL**: Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving (2025) [](https://openreview.net/forum?id=MhBZzkz4h9) |
@@ -230,7 +226,7 @@ Based on a systematic review of **176 papers and online resources**, this survey |
230 | 226 |
|
231 | 227 | ### 📚 Supervised Fine-Tuning (SFT) |
232 | 228 |
|
233 | | -*Models trained via supervised learning* |
| 229 | +*Models trained via supervised fine-tuning* |
234 | 230 |
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235 | 231 | - **Lingma SWE-GPT**: SWE-GPT: A Process-Centric Language Model for Automated Software Improvement (2025) |
236 | 232 | - **ReSAT**: Repository Structure-Aware Training Makes SLMs Better Issue Resolver (2024) [](https://arxiv.org/abs/2412.19031) |
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