Topic models are a class of unsupervised learning algorithms for detecting the semantic
structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, …

Software visualization techniques provide effective means for program comprehension
tasks as they allow developers to interactively explore large code bases. A frequently …

In order to detect software risks at an early stage, various software visualization techniques
have been developed for monitoring the structure, behaviour, or the underlying development …

Standard datasets are frequently used to train and evaluate Machine Learning models.
However, the assumed standardness of these datasets leads to a lack of in-depth discussion on …

Standard datasets are frequently used to train and evaluate Machine Learning models.
However, the assumed standardness of these datasets leads to a lack of in-depth discussion on …

The number of publicly accessible software repositories on online platforms is growing rapidly.
With more than 128 million public repositories (as of March 2020), GitHub is the world’s …

Based on the assumption that semantic relatedness between documents is reflected in the
distribution of the vocabulary, topic models are a widely used class of techniques for text …

The number of software projects developed collaboratively on social coding platforms is
steadily increasing. One of the motivations for developers to participate in open-source software …

The semantic similarity between documents of a text corpus can be visualized using map-like
metaphors based on two-dimensional scatterplot layouts. These layouts result from a …

Text spatializations for text corpora often rely on two-dimensional scatter plots generated
from topic models and dimensionality reductions. Topic models are unsupervised learning …