The article explores how machine learning can help analyze computer programs by using high-dimensional vectors called neural source code embeddings. The researchers studied code2vec embeddings and found that they perform similarly to handcrafted features in classifying programs. They also discovered that code2vec embeddings distribute information more evenly and are more robust when some dimensions are removed. This research aims to improve our understanding of these code representations.