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Found 4 papers, 2 papers with code
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development.
Predicting Code Coverage without Execution
Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code.
LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification
In this work, we describe how we created the dataset, created annotations at high level and low level for different domains and how we use it to test the current state-of-the-art multilingual and multitask learning approaches.
Training and Evaluating a Jupyter Notebook Data Science Assistant
We study the feasibility of a Data Science assistant powered by a sequence-to-sequence transformer by training a new model JuPyT5 on all publicly available Jupyter Notebook GitHub repositories and developing a new metric: Data Science Problems (DSP).
