Overview
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:
Papers
| Date | Title | Presenter | Slides | Advisor |
|---|---|---|---|---|
| Sep 24 | Introduction to the seminar (topics, objectives, structure): | Dr. Veselin Raychev |
|
|
| 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 |