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:


Feb 18 Introduction to the seminar (topics, objectives, structure): Dr. Veselin Raychev PDF
Mar 04 Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code Rubin Deliallisi Pavol
Mar 11 Parameter-Free Probabilistic API Mining across GitHub Bernhard Kellermann Timon
Tracelet-Based Code Search in Executables Stewart Webb Marc
Mar 18 Compilation Error Repair: For the Student Programs, From the Student Programs Benjamin Schmid Mislav
Synthesizing Programs for Images using Reinforced Adversarial Learning Zacharias Fisches Matthew
Mar 25 RobustFill: Neural Program Learning under Noisy I/O Svilen Stefanov Timon
Dynamic Neural Program Embeddings for Program Repair Roman Haag Marc
Apr 01 Learning Loop Invariants for Program Verification Tierry Hörmann Mislav
Selecting Representative Examples for Program Synthesis Christian Bräm Pavol
Apr 15 Tree-to-tree Neural Networks for Program Translation Mounir Amrani Pavol
Estimating Types in Binaries using Predictive Modeling Yuanhao Huang Veselin
Apr 29 Neural Guided Constraint Logic Programming for Program Synthesis David Yenicelik Matthew
PHOG: Probabilistic Model for Code Sven Knobloch Marc