Machine Learning for Software Engineering: modelling the source code

09:00-12:30, January 26 @ Foyer 6

Workshop / Overview

Machine Learning on Source Code (MLonCode) is an emerging research domain which stands at the intersection of deep learning, natural language processing, software engineering and programming language communities.

We will review recent SE tasks that benefit from applying ML and focus hands-on experience on extracting data from the real source code and developing multiple different models for a particular task of source code summarization (or function name suggestion).

Workshop / Outcome

At the end of the workshop participants will build 2 working models on a real dataset, producing near state-of-the-art results. Practical skill of extracting information from source code as well as modeling different aspects of it are going to be acquired.

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Familiar with the basics of DeepLearning
  • Own laptop
  • Docker installed

Track / Co-organizers

Alexander Bezzubov

Machine Learning Research Engineer

Hugo Mougard

Senior Machine Learning Engineer

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