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:
|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|