[I'm one of the Microsoft Research people who worked on this] There are many interesting ideas that you could build on top of this kind of data, and we only sc

[I'm one of the Microsoft Research people who worked on this] There are many interesting ideas that you could build on top of this kind of data, and we only scratched the surface so far. For example, the simple "search" technique we are using as a baseline is based on the idea of joint embeddings: We learn functions f_query and f_js/f_python/... to map from the inputs into some vector space such that, for example, for a (python method, docstring) pair, f_query(docstring) is near to f_python(method). To search given a query, we just do f_query(query) and look for nearest neighbours in all the code we indexed before. Now, we could also just do f_python(def bubblesort(...): ...) and look for the nearest neighbour that is in C#, and should get out a C# implementation of bubblesort. Similarly, we could apply all kinds of filters on the results (code from highly-starred repos, code that uses framework X, ...) to do more interesting thing.