Aroma: Using machine learning for code recommendation

When an engineer writes a new code snippet, Aroma creates a sparse vector in the manner described above and takes the dot product of this vector with the matrix containing the feature vectors of all existing methods. After obtaining a list of candidate code snippets in descending order of similarity to the query, Aroma runs an iterative clustering algorithm to find clusters of code snippets that are similar to each other and contain extra statements useful for creating code recommendations. Other code recommendations are created from other clusters in the same way, and Aroma’s algorithm ensures that these recommendations are substantially different from one another, so engineers can learn a diverse range of coding patterns by looking at just a few code snippets.

Source: ai.facebook.com