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:


22.02 Introduction to the seminar (topics, objectives, structure): Veselin Raychev PDF
Candidate papers to choose from
08.03 LambdaNet: Probabilistic Type Inference using Graph Neural Networks Ahmed Bouhoula Matthew
15.03 Deep Learning based Vulnerability Detection: Are We There Yet? Julien Tinguely Max
Synthesizing Programs for Images using Reinforced Adversarial Learning Julia Bazinska Mark
22.03 Estimating Types in Binaries using Predictive Modeling Laurin Brandner Veselin
Unsupervised Translation of Programming Languages Fredin Thazhathukunnel Marc
29.03 Neural Software Analysis Lam Nguyen Thiet Luca
12.04 Learning semantic program embeddings with graph interval neural network Remo Geissbühler Max
Programmatically Interpretable Reinforcement Learning Gabriela Krasnopolska Mark
26.04 PHOG: Probabilistic Model for Code Mike Boss Marc
Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks Jason Friedman Johannes
03.05 Neural Guided Constraint Logic Programming for Program Synthesis Jannis Bolik Matthew
CodeBERT: A Pre-Trained Model for Programming and Natural Languages Sven Wiesner Johannes