General description

Software repositories archive valuable software engineering data, such as source code, execution traces, historical code changes, mailing lists, and bug reports. This data contains a wealth of information about a project’s status and history. Doing data science on software repositories, researchers can gain empirically based understanding of software development practices, and practitioners can better manage, maintain, and evolve complex software projects.

In the recent years, the advances in Machine Learning and AI technologies, as demonstrated by the successful application of Deep Neural Networks in various domains did not go unoticed in the field of Software Engineering. Researchers have applied DNNs to tackle issues such as automated program repair, code summarization, code structure representation, etc.

IN4334 is a seminar course that aims to give students a deep understanding of and hands-on approach on how deep neural networks and NLP techniques are used by today’s industry leaders to represent knoweledge and solve existing problems in novel ways.

Learning Objectives

This course will enable students to:

Course Organization

The course projects

During the course, you will choose a software engineering problem and will propose a ML-based solution for that problem.

See the list of suggested projects (and existing papers that try to tackle it). Your job will be to either:

  1. Replicate an existing paper: Replication is a topic much touted but seldom practiced in the software engineering community. It is, however, a core aspect of science, especially empirical. You can download readily available data sets published together with the paper, requesting the data from the original authors or by applying the same techniques on different data.

  2. Propose a completely new approach to the problem (highly appreciated!). Did you find a way to improve the existing work? Did you see the problem from a perspective that current research hasn’t explored yet? Your task will be to collect data and test your hypothesis.

You will implement different ML/DL models. You are required to use Python and more specifically, Pytorch. Check our curated list of tutorials that might help you in getting started with different NLP, DL, and ML topics.

Required reading for week 1:


Date Week Lecture Topic Lecturer
3/9 1 1 Course Introduction, How to read a paper in a group GG
4/9 1 2 Enough data science to become dangerous, notebook GG
11/9 2 1 Enough neural networks to become dangerous, Hand-written number recognition with PyTorch, Sing like Freddie via LSTMs notebook MFA
12/9 2 2 Enough NLP to become dangerous, Building your ML pipeline MFA / GG
17/9 3 1 Representing code Group 3
18/9 3 2 Code embeddings Group 8
24/9 4 1 Source code analysis Group 7 / VM
25/9 4 2 NLP-based program analysis Group 11 / VM
1/10 5 1 Finding bugs Group 4 / MA
2/10 5 2 Code summarization Group 5 / MA
8/10 6 1 Feedback session MFA / GG
9/10 6 2 Code Completion Group 1
15/10 7 1 Transfer Learning Group 2 / VK
16/10 7 2 Repairing bugs Group 6 / VK


Guest lecturers


The final course grade will be calculated as:

All deliverables will be peer-reviewed by 2 other teams. The peer-review grade is 50% of the final grade per grade item.


M. Pradel and K. Sen, “DeepBugs: A learning approach to name-based bug detection,” Proc. ACM Program. Lang., vol. 2, no. OOPSLA, pp. 147:1–147:25, Oct. 2018.