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Found 6 papers, 2 papers with code
Studying Vulnerable Code Entities in R
Pre-trained Code Language Models (Code-PLMs) have shown many advancements and achieved state-of-the-art results for many software engineering tasks in the past few years.
On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules
Although adapters are known to facilitate adapting to many downstream tasks compared to fine-tuning the model that require retraining all of the models' parameters -- which owes to the adapters' plug and play nature and being parameter efficient -- their usage in software engineering is not explored.
On the Effectiveness of Pretrained Models for API Learning
We have also identified two different tokenization approaches that can contribute to a significant boost in PTMs' performance for the API sequence generation task.
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation
In this paper, we evaluate PTMs to generate replies to the mobile app user feedbacks.
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews
In addition, we investigate the performance of the PTMs trained on app reviews (i. e. domain-specific PTMs) .
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations
However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input.
