Course Introduction

In this course, we will learn

• What machine learning for SE is
• What you can do with it
• How to do it

We will also learn

• How to read papers critically (this lecture!)
• How to discuss papers
• How to write SE papers
• How to do ML4SE in practice

Course Teachers

• Georgios Gousios
• Maurício Aniche

Teaching assistants:

• Malileh Izadi
• Amir Mir
• Elvan Kula
• Jeanderson Barros Cândido

How will we study

The course is a seminar / project based. Therefore:

• We will study papers, lots of them!
• We will write nice summaries
• We will present the results of our study in the classroom
• We will work in groups (5-6 people)
• We will develop ML methods for SE problems

How to get a paper

• At Uni!

• Via VPN (to Uni). TU Delft has contracts with most major Tech publishers to get you free access to papers.

Q How much does one article cost?

• There are alternatives …

We use Google Scholar to retrieve metrics. There is other tools such as Scopus. Even the IEEE Xplore Digital Library gives metrics.

Course deliverable: ML solution 4 SE problem

To finish this course:

• Select one of those research topics
• Use ML (however advanced or naive) to solve it. No heuristics allowed!
• Submit your paper + code + blog post
• Present your solution in front of the group

Grading

• 80% paper
• 10% final presentation
• 10% blog post
• P/F peer review

Deadlines

• Selection of topic: 4th Sep (this upcoming Friday)
  • Students with no group or topic will not be allowed to take the course.
• First version of pipeline: 28th Sep
• Report + solution: 21rd Oct
• Peer-review: 26th Oct
• Final paper + blog post: 29th Oct
• Presentation: 30th Oct

TODO for today

• Form a group. Enrol yourself in a group in Brightspace.
• Explore the project ideas and select a topic.
• Post your project idea in the “Project proposals” forum in Brightspace.
• Join our Mattermost channel

Lecture notes

• Freely available on the web, on Georgios’s homepage (http://gousios.gr/courses/ml4se/)

• You can print/download them before the lecture and bring them along to make additional notes.

• We are looking forward to improve them! If you have suggestions, find mistakes etc, let us know!

Bibliography

Copyright

The course contents are copyrighted (c) 2018,2019,2020 - onwards by TU Delft and their respective authors and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.