Overview

The objective of the seminar is to:

  • Introduce students to the field of Deep Learning for Big Code.
  • Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods.
  • Highlight the latest research and work opportunities in industry and academia available on this topic.

The seminar is carried out as a set of presentations (2 each lecture) chosen from a set of available papers (available below). The grade is determined as a function of the presentation, handling questions and answers, and participation:

Papers

DateTitlePresenterSlidesAdvisor
Introduction to the seminar (topics, objectives, structure): Martin Vechev PDF
March 12 DeepCoder: Learning to Write Programs Patrick Schmidt PDF Veselin Raychev
A Bimodal Modelling of Source Code and Natural Language Sandro Marcon PDF Pesho Ivanov
March 19 Detecting object usage anomalies Flavia Cavallaro PDF Benjamin Bichsel
sk_p: a neural program corrector for MOOCs Andrea Rinaldi PDF Pavol Bielik
March 26 Probabilistic Model for Code with Decision Trees Robin Vaaler PDF Timon Gehr
April 9 Code Completion with Neural Attention and Pointer Networks Ondrej Skopek PDF Pavol Bielik
Learning to Represent Programs with Graphs Mislav Balunovic PDF Benjamin Bichsel
April 23 Melford: Using Neural Networks to Find Spreadsheet Errors Hlynur Freyr PDF Pesho Ivanov
A Convolutional Attention Network for Extreme Summarization of Source Code Jovan Andonov PDF Veselin Raychev