From a machine learning perspective (i.e. theoretically according to my view) this is useful because source code is highly structured with very complex constraints and tons of data available (e.g. every project in GitHub). This means that machine-learning methods for handling such problems need to be developed. Such methods may eventually be useful in other application.

Now on the applied side (software engineering, programming languages), such methods (probabilistic machine learning and probabilistic/statistical models) can handle uncertainty with a principled way and provide useful tools to software engineers that exploit the amounts of data that is available both in internal and external codebases. This is something that is not 100% possible with formal tools, that usually require some form of human knowledge to be embedded. For example, you will see on the list above, tools that can do autocompletion, others that suggest "reasonable" renamings or others that help migration of source code between languages and all thanks to data.

Hopefully, at some point these methods will be so advanced that they will be able to learn (i.e. trained) from every piece of code that is available online and (e.g.) spot bugs in your code, semi-automatically refactor your code etc.