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.

IN4334 is a seminar course that aims to give students a deep understanding of and hands-on approach on software analytics.

Learning Objectives

This course will enable students to:

Course Organization

The course projects

During the course, the students will engage in 2 collaborative projects:

Survey of software analytics

Every year, tens of papers are published in the area of software analytics. This leads to a high noise to signal ratio: lots of papers containing marginal insights. For outsiders, it is really difficult to obtain an overview of what software analytics have to offer to software projects.

To make things easier for newcomers, we will collaborative work on a high-quality summary of the area, outlining the current state of the art and future challenges. To make this work, the course instructor will provide an outline of the area, pointers to important papers and a paper skeleton; students will have to summarize a sub-area of software analytics.

Task duration: 5 weeks

Replicating existing work

Replication is a topic much touted but seldom practiced in the mining software repositories and the software analytics communities. It is, however, a core aspect of science, especially empirical.

The purpose of this task is to attempt a replication of a recent paper, either by downloading readily available data sets published together with the paper, requesting the data from the original authors or by applying the same techniques on a different sample. You will select a paper from the list that you studied for your literature survey.

Task duration: 5 weeks

Study material

The following material is a-must-read in the study of software analytics.


Date Week Lecture Topic Lecturer
3/9 1 1 Course Introduction, Quantitative methods in Software Engineering MB
5/9 1 2 Discussion Groups GG
10/9 2 1 Process Analytics AR / GG
12/9 2 2 Testing Analytics students, MB
17/9 3 1 Build Analytics students, MB
19/9 3 2 Bug Prediction students, MB
24/9 4 1 Software Ecosystem Analytics students, JH
26/9 4 2 Release Engineering Analytics students, AR
1/10 5 1 Results: Survey on Software Analytics students
3/10 5 2 Code Review students, GG
8/10 6 1 Runtime and Performance Analytics, Cross-review of surveys MK
10/10 6 2 App Store Analytics students, MK
15/10 7 1 Analytics at Work: ING Hennie Huijgens
17/10 7 2 Results: Replication project results students



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. The results add up to 110%.


[1] 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.