The objective of the seminar is to:

  • Introduce students to the emerging 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:


Sep 24 Introduction to the seminar (topics, objectives, structure): Dr. Veselin Raychev PDF
15.10 Parameter-Free Probabilistic API Mining across GitHub Anastasia Sycheva Inna Grijnevitch
Code Completion with Neural Attention and Pointer Networks Ylli Muhadri Jingxuan He
22.10 Learn&Fuzz: Machine Learning for Input Fuzzing Nicolas Mesot Pesho Ivanov
An Encoder-Decoder Framework Translating Natural Language to Database Queries Ming-Da Liu Zhang Pavol Bielik
29.10 Predicting Program Properties from "Big Code" Panayiotis Panayiotou Dana Drachsler Cohen
05.11 RobustFill: Neural Program Learning under Noisy I/O Momchil Pavlinov Peychev Benjamin Bichsel
DeepBugs: A Learning Approach to Name-based Bug Detection Pietro Constante Oldrati Samuel Steffen