Machine Learning on Source Code

Machine Learning is definitely what the cool kids are doing nowadays. Deep Learning specifically powered a revolution on many fields of research, including Computer Vision and Natural Language Processing, but also self driving cars, or strategy games like Go. What not many are talking about is how to those techniques to improve our developer routines. Machine Learning on Source Code (MLonCode) is a very interesting field because it is at the frontier of Natural Language Processing, Graph-Based Machine Learning, Static Analysis, and has the power to even bring other fields like Dynamic Analysis of programs. The amount of data available for this problem is almost overwhelming, and given that data is the fuel of Machine Learning, we are excited for an amazing ride! This talk will cover the basics of what Machine Learning techniques can be applied to source code, specifically we will discover: * embeddings over identifiers, * structural embeddings over source code, answering the question how similar are two fragments of code, * recurrent neural networks for code completion, * future direction of the research. While the topic is advanced, the level of mathematics required for this talk will be kept to a minimum. Rather than getting stuck in the details, we'll discuss the advantages and limitations of these techniques, and their possible implications to our developer lives.