Capita Selecta of Software Engineering

Machine Learning for Software Engineering

The goal of the course is to analyze large amounts of software engineering data using data mining techniques to uncover interesting and actionable information about software systems and projects. We use modern tools and techniques for mining this data in order to discuss the associated challenges and outline future research directions.

Examples include bug prediction using classifiers, search-based software engineering, and pattern mining of git repositories.

In the final lectures, we study recent research to understand how the mining of software repositories is evolving. For these lectures, the students will prepare a presentation on which they will be graded.

The goal of the practical sessions is to apply and to extend state-of-the art methodologies and tools to real software projects. Students will be graded on three assignments in which they will extend state-of-the-art frameworks.

Table of Contents

Examination

There is no traditional oral or written exam. Students will be graded as follows:

• 3 mandatory assignments in which students apply mining software repositories techniques on real software systems (20% each), assigned during the lectures.
• 1 mandatory oral presentation synthesizing two recent publications in the domain (40%), to be chosen from predetermined list.
  Deadline: last lectures of the course.

Note that failing to hand in an assignment or failing to present automatically results in an ABSENT mark.