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Found 11 papers, 3 papers with code
Do Current Language Models Support Code Intelligence for R Programming Language?
Though these models have achieved the state-of-the-art performance for SE tasks for many popular programming languages, such as Java and Python, the Scientific Software and its related languages like R programming language have rarely benefited or even been evaluated with the Code-PLMs.
A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
This survey serves as a resource for researchers and practitioners at the intersection of LLMs, software engineering, and specialized programming languages, laying the groundwork for future advancements in code generation for LRPLs and DSLs.
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair
[Context] Large Language Models (LLMs) have shown good performance in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing.
StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation
Developers spend much time finding information that is relevant to their questions.
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R
We will assess their performance compared to fully fine-tuned models, whether they can be used for knowledge transfer from natural language models to code (using T5 and Llama models), and their ability to adapt the learned knowledge to an unseen language.
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.
