Data Science for Software Engineering (ds4se) is an academic initiative to perform exploratory and causal inference analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.
It is required an interpretability module for the lower space dimensionality analysis of vectorized software artifacts. Input: a data frame with a column whose values are tensor representations of an artifact.
Output: a diagram with the spatial distribution of the prototypes and criticisms.
Tasks:
[ ] Use a module to reduce the dimensionality of a tensor (e.g., t-sne, pca, or t-sne with pca)
[ ] Colour every tensor point according to the nature of the artifact (e.g., blue for requirements, yellow for source code, green for test cases, etc)
[ ] Run P&C (or K-medoids) on the reduced dimensionality. Please reduce the data two-fold: 2-D and 3-D
[ ] How are the data grouped?
[ ] Render the value of self-information (or Artifact Entropy) along with the 2-D plot. How can we visualize entropy vs semantics?
It is required an interpretability module for the lower space dimensionality analysis of vectorized software artifacts.
Input: a data frame with a column whose values are tensor representations of an artifact. Output: a diagram with the spatial distribution of the prototypes and criticisms.
Tasks: