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