Recent years have witnessed increasing interest in code representation learning, which
aims to represent the semantics of source code into distributed vectors. Currently, various …

The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of
centralized model hubs like Hugging Face, which facilitate collaborative development and …

Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line
visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating …

The development and training of deep learning models have become increasingly costly
and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for …

Data science pipelines to train and evaluate models with machine learning may contain
bugs just like any other code. Leakage between training and test data can lead to …

Jupyter notebooks have emerged as the predominant tool for data scientists to develop and
share machine learning solutions, primarily using Python as the programming language …

Deep Learning (DL) has been widely applied in various fields. Unlike traditional software,
DL programs possess the “black box” characteristic that can make it challenging for …

The success of machine learning (ML) models depends on careful experimentation and
optimization of their hyperparameters. Tuning can affect the reliability and accuracy of a …

The Python community strives to design pythonic idioms so that Python users can achieve
their intent in a more concise and efficient way. According to our analysis of 154 questions …

While Python is increasingly popular, program analysis tooling for Python is lagging. This is
due, in part, to complex features of the Python language---features with difficult to …