Machine Learning for Software Engineering

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.  
Advances in Machine Learning and AI technologies, as demonstrated by the successful application of Deep Neural Networks in various domains did not go unnoticed in the field of Software Engineering; researchers have applied machine learning to tackle different software engineering tasks. This course aims to give students a deep understanding of and hands-on approach on how deep neural networks and NLP techniques are used to represent knowledge and solve existing SE problems in novel ways. 

The final grade of the course consists of the following components:

• Group Assignments: Project LLM4code report, including code. Propose an idea for improvement of an existing language model for code and implement and assess the same model (weighting 60%)

• Group Presentation 1: Project LLM4code presentation: Report and discuss results of the project (weighting 20%)

• Individual Presentation: Individual questions and answers about the project LLM4code (weighting 10%)

• Individual Assignment: Coding assignment and multiple choice questions (weighting 10%)

• Group Presentation 2: Present a state-of-the-art approach in the literature (pass/fail)

Final grade calculation = 0.7 * Group Assignments + 0.2 * Group Presentation 1 + 0.1 Individual Presentation + 0.1 * Individual Assignment + Pass/Fail * Group Presentation 2

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Group Assignments: Repair opportunity

• Group Presentation 1: Resit opportunity

• Individual Presentation: Resit opportunity

• Individual Assignment: Resit opportunity

• Group Presentation 2: Resit opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).