ERASMUS Traineeship Offers, Academic Year 2018-2019

Below is a list of project topics for ERASMUS Traineeship topics offered by the software engineering research group in 2018 for students who intend to defend in June 2019. New topics for students planning to defend in 2020 will be offered soon.

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Fraunhofer IESE, Kaiserslautern, Germany

Link: https://www.iese.fraunhofer.de/en.html

Send expression of interest to:

Dietmar Pfahl (dietmar [dot] pfahl [at] ut [dot] ee)

Contacts:

University of Tartu: Dietmar Pfahl (dietmar [dot] pfahl [at] ut [dot] ee)
Fraunhofer IESE: Andreas Jedlitschka (andreas [dot] jedlitschka [at] iese [dot] fraunhofer [dot] de)

Description:
In the context of a large international research project with industry partners, Fraunhofer IESE in Kaiserslautern, Germany, is developing methods and tools for data-driven quality management of embedded software systems at runtime as well as data-driven approaches for managing safety critical systems. In the context of this project, several student projects can be defined based on the interests and background of interested students.

Topic 1:

Usage-data driven quality measurement at runtime

Requirements:

Strong Java skills (optional: Python/R)
Interest in Data Mining / Machine Learning / AI

Competences to be acquired:

Knowledge on how to apply Data Science in the context of software project management
Data-driven Quality Management
Learning how to work in a heterogeneous team
Learning to apply state of the art artificial intelligence approaches

Topic 2:

Data-driven approaches for safety-management of autonomous systems

Requirements:

Strong Programming skills (Java / C# and optional: Python/R)
Interest in Data Mining / Machine Learning / AI
Interest in autonomous systems

Competences to be acquired:

Knowledge on how to apply Data Science in the context of safety-critical, autonomous systems
Data-driven Safety Management
Learning how to work in a heterogeneous team
Learning to apply state of the art artificial intelligence approaches to

Software Competence Center Hagenberg GmbH (SCCH), Hagenberg, Austria

Link: https://www.scch.at/en/news

Send expression of interest to:

Dietmar Pfahl (dietmar [dot] pfahl [at] ut [dot] ee)

Contacts:

University of Tartu: Dietmar Pfahl (dietmar [dot] pfahl [at] ut [dot] ee)
SCCH: Rudolf Ramler (rudolf [dot] ramler [at] scch [dot] at)

Topic:

Predicting Survived and Killed Mutants

Here is a true story:
A software developer worked in the software development department of a large company. When the developers had a new software version ready, it had to be sent to the testing department. The two departments did not have the best relationship, since whenever the testers found bugs it made the developers look bad. So, to make fun of the testers, the developers intentionally added a few bugs to the software before sending it to testing.
And after the testers were done, the developers would point out to the testers what bugs they had missed. What the developers did not know is that there exists a method called “mutation analysis” and tools doing the exact same thing: Making small changes to code producing “mutants”, run the tests, and see what changes have been detected (“killed mutants”) or missed (“survived mutants”).
The problem is that usually thousands of mutants can be generated and the tests have to be re-executed for each of them. Thus, instead of re-running all tests, we propose using data mining to predict which mutants will be killed or survive. The development of such a prediction model is the task of this student project.

Requirements:

Programming skills (preferably C/C++)
General knowledge on data mining

Competences to be acquired:

Knowledge on feature extraction from source code
Mutation testing

Related literature and tool:
[1] R. Ramler et al. "An empirical study on the application of mutation testing for a safety-critical industrial software system." Symposium on Applied Computing (SAC), 2017.
[2] Y. Jia, M. Harman. "MILU: A customizable, runtime-optimized higher order mutation testing tool for the full C language." TAIC PART, 2008. https://github.com/yuejia/Milu

Lana Labs, Berlin, Germany

Link: https://lana-labs.com/en/

Send expression of interest to:

Marlon Dumas (marlon [dot] dumas [ät] ut [dot] ee)

Lana Labs is a young and growing company that develops and commercializes a tool for process mining. The tool is paticularly well-known for its conformance checking features, which makes it a tool of choice for conducting business process audits.

The intern will work within the company's product engineering team, implementing new features for root-cause analysis of deviations in business process executions by combining machine learning techniques with the existing process mining techniques implemented in the LANA product.

The intern is expected to have working knowledge of either Scala or Java, good knowledge of functional programming, and working knowledge of data mining/machine learning.

The company is willing to offer a remuneration to the sucessful applicant (on top of any Erasmus+ travel allowance).

SIAV, Padova, Italy

First point of contact:

Fabrizio Maggi(fabrizio[dot] maggi [at] ut [dot] ee)

Siav is an Italian software development and IT services company specialized in electronic document management and digital business processes. Siav offers its specialized expertise gained in the implementation of complex projects and stands out for its ability to ensure its own resources for analysis activities, implementation, customization, training and support.

Two of SIAV's flagship products are Archiflow (an enterprise content management system) and BIPOD (a business process intelligence platform). In this internship, you will contribute to one of these two products, depending on your interests, background, and our development priorities. Good knowledge of .Net or Java are highly desirable.

The company might be willing to offer a remuneration to the sucessful applicant (on top of any Erasmus+ travel allowance) in case the applicant has significant development experience (to be negotiated case by case).