Deep Learning for Big Code

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	Advisor
Sep 23	Introduction to the seminar (topics, objectives, structure):	Dr. Veselin Raychev
7.10	No class	No class
14.10	When Deep Learning Met Code Search	Siméone de Fremond	Marc Fischer
	code2seq: Generating Sequences from Structured Representations of Code	Peter Grönquist	Timon Gehr
21.10	No class	No class
28.10	Commit Message Generation for Source Code Changes	Sandro Luck	Matthew Mirman
	NL2Type: Inferring JavaScript Function Types from Natural Language Information	Carlos Gomes	Pavol Bielik
4.11	Mining Version Histories to Guide Software Changes	Gabriela E. López Magaña	Mislav Balunović
	Getafix: Learning to Fix Bugs Automatically	Lukas Ernst	Matthew Mirman
11.11	Statistical Reconstruction of Class Hierarchies in Binaries	Laurin Paech	Pavol Bielik
	Neural Program Synthesis from Diverse Demonstration Videos	Raphaël Dang-Nhu	Veselin Raychev
18.11	DeepBugs: A Learning Approach to Name-based Bug Detection	Tobias Lorenz	Matthew Mirman
	An Encoder-Decoder Framework Translating Natural Language to Database Queries	Luca Beurer-Kellner	Veselin Raychev
25.11	DeepCoder: Learning to Write Programs	Andrej Ivanov	Timon Gehr
	Neural Program Repair by Jointly Learning to Localize and Repair	Marko Mihajlovic	Mislav Balunović