Deep Learning for Big Code

Course no.
263-2926-00L
Semester
Spring 2018
Lecturer
Prof. Dr. Martin Vechev
Head TA
Dr. Veselin Raychev
EDoz
Link
Time
Monday, 16-18
Place
CHN D48
Credits
2


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:

DateTitlePresenterSlidesAdvisor
Introduction to the seminar (topics, objectives, structure): Martin Vechev PDF
Bayesian Specification Learning for Finding API Usage Errors. PDF
Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code PDF
Detecting object usage anomalies PDF
Learning from 6,000 projects: lightweight cross-project anomaly detection PDF
Mining Version Histories to Guide Software Changes PDF
sk_p: a neural program corrector for MOOCs PDF
A Convolutional Attention Network for Extreme Summarization of Source Code PDF
A Bimodal Modelling of Source Code and Natural Language PDF
Code Completion with Neural Attention and Pointer Networks PDF
DeepCoder: Learning to Write Programs PDF
Learning to Represent Programs with Graphs PDF
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection PDF
Parameter-Free Probabilistic API Mining across GitHub PDF
Tracelet-Based Code Search in Executables PDF
Learning Program Embeddings to Propagate Feedback on Student Code PDF
Learning a Static Analyzer from Data PDF
Estimating Types in Binaries using Predictive Modeling PDF
Convolutional Neural Networks over Tree Structures for Programming Language Processing PDF
Predicting Program Properties from "Big Code" PDF
Probabilistic Model for Code with Decision Trees PDF
DeepFix: Fixing Common C Language Errors by Deep Learning. PDF
Melford: Using Neural Networks to Find Spreadsheet Errors PDF