# Mining on Students' Execution Logs and Repairing Compilation Errors Based on Deep Learning
## 3 Mining on Students' Execution Logs
While large models have shown promising results, their resource-intensive nature for training and deployment poses challenges. In light of these challenges, future research directions could focus on using large models to assist smaller models and enhance the accuracy of their fine-grained repair capabilities. This approach could involve a multi-model framework where a large model is used to guide and augment the training process of smaller models. By exploiting the strengths of large models, such as their ability to capture complex code semantics, smaller models can benefit from their knowledge and refine their repair functions. This approach may lead to more efficient and effective code repairs with better accuracy. ## 8 Conclusions

This paper presents a method for mining reasonable error-repair program sample pairs from students' program execution logs in the online experimental system, providing a way to acquire a labeled dataset of real program errors. To address the issue of limited error patterns and unrealistic error contexts in the perturbed generated dataset, this study proposes the RS to generate a large number of program pairs with more semantic information that better align with real error contexts. To tackle low-frequency errors and complex error patterns in program repair, the ACNPR model is introduced, which integrates compiler feedback information. Through comparative experiments conducted on three different datasets, the effectiveness of the repair model is validated. Our approach utilizes a dataset obtained from students' submissions in programming tasks, which predominantly consists of errors commonly made by students and novice programmers. This allows us to provide timely assistance to students during their programming process by locating specific errors and suggesting appropriate modification strategies. This approach aims to enhance students' learning efficiency while reducing the workload for teachers. However, it is important to note that at present, our method focuses primarily on fixing compilation errors. Therefore, the predicted repair patches cannot guarantee the successful completion of programming tasks. In the future, we plan to incorporate semantic constraints targeting program functionality to generate repair patches that are more likely to be semantically correct. This will effectively guide students in completing programming tasks and foster a more proactive learning experience. Conceptualization, R.S. and J.H. ; methodology, R.S. ; software, R.S. and B.L. ; validation, R.S. and B.L. ; writing--original draft preparation, R.S. ; writing--review and editing, J.H. ; project administration, J.H. ; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Shanghai Collaborative Innovation Center of data intelligence technology, grant number 0232-A1-8900-23-09. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest. ## References

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