TU Delft
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2018/2019 Electrical Engineering, Mathematics and Computer Science Master Computer Science
Software Analytics
Responsible Instructor
Name E-mail
Dr.ir. G. Gousios    G.Gousios@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Expected prior knowledge
- Experience with programming is required.
- Experience with research methods is nice to have
Course Contents
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.
Study Goals
This course explores techniques and leading research in mining Software Engineering data, discusses challenges associated with mining SE data, highlights SE data mining success stories, and outlines future research directions. Students will acquire the knowledge needed to perform research or conduct practice in the field. Once completed, students should be able to do data science on software repositories in their own research or practice.

This course will enable students to:

- Understand and analyze related work in the area of software analytics
- Apply appropriate research methods to extract data from software repositories
- Evaluate the applicability of results in the software analytics literature on practical problems
- Analyze data from software repositories and extract new insights
Education Method
The course is a seminar, which means that we will be studying the literature in the area of software analytics. The course consists of the following education methods:

- Lectures by experts in various areas of software analytics
- Self-study and presentation of papers
- Development of tools to extract data from software repositories

To finish the course, students (in groups) will have to:

- Study research papers (15+) and provide a high quality 2-3 page summary, to be included in a shared survey of the area of software analytics
- Present a selected paper in classroom
- Implement a data acquisition/analysis plug-in for a software analytics platform
Literature and Study Materials
Slides and research papers will be at the basis of the course. The teacher will share these resources as study materials with the students.

All lecture materials can be found here: http://gousios.org/courses/softwanal/
The following list is indicative

- C. Bird, T. Menzies, and T. Zimmermann, The art and science of analyzing software data, 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2015.
- Menzies, Tim, Laurie Williams, and Thomas Zimmermann. Perspectives on Data Science for Software Engineering. Morgan Kaufmann, 2016.
The final grade consists of the following items:

* 50% - Quality of survey contribution
* 30% - Implementation of the CodeFeedr plug-in
* 20% - Presentation of related work

The course does not have an exam and there will be no resit of any of the items above.
Enrolment / Application
Each student who wants to take part in this course is *required* to:
- register/enrol on Brightspace before the start of the course
- participate in the first lecture of the course

Failure to comply with these requirements may lead the student to be not allowed to take part in the course.
Research Methods
Software Engineering