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:


21.09 Introduction to the seminar (topics, objectives, structure): Veselin Raychev PDF
05.10 code2vec: Learning Distributed Representations of Code Agon Pesho Ivanov
Structural Language Models of Code Afra Mislav Balunović
12.10 Scaffle: Bug Localization on Millions of Files Yann Marc Fischer
Neural Attribution for Semantic Bug-Localization in Student Programs Daniele Momchil Peychev
19.10 LambdaNet: Probabilistic Type Inference using Graph Neural Networks Sinan Pesho Ivanov
Learning Loop Invariants for Program Verification Robin Mislav Balunović
26.10 Synthesizing Programs for Images using Reinforced Adversarial Learning Arda Marc Fischer