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:
Schedule
| Date | Title | Presenter | Slides | Advisor |
|---|---|---|---|---|
| 21.09 | Introduction to the seminar (topics, objectives, structure): | Veselin Raychev |
|
|
| 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 |