Research Group for Applied Software Engineering
Forschungsgruppe für Angewandte Softwaretechnik

Joint Advanced Student School - JASS 2019

Smart Software for the Smart Industry

JASS Participants at Zeiss JASS Participants at TUM

Course Organizers

Prof. Kirill Krinkin
Prof. Bernd Bruegge
Prof. Kirill Krinkin
(JetBrains Research, ETU LETI)
Prof. Bernd Brügge
(TUM, Chair for Applied Software Engineering)


Matthias Gohl
Dr. Nicolas Bensaid
Sabrina Senna Dr. Lydia Nemec Kay-Uwe Clemens
Matthias Gohl Dr. Nicolas Bensaid Sabrina Senna Dr. Lydia Nemec Kay-Uwe Clemens

Project Leaders

Jan Philip Bernius Sajjad Taheri Paul Schmiedmayer

Film Team

Ruth Demmel Andreas Jung



JASS 2019 consists of three projects: Cataract, Augmented Reality and Predictive Maintenance. Each of the projects is described in terms of four major activities shown below.

Project Cataract

(Project videos ommitted due to non-disclosure agreement)


(Project description ommitted due to non-disclosure agreement)

Customers: Nicolas Bensaid, Hristina Srbinoska

Project Leader: Jan Philip Bernius

Developers: Anastasiia Murzina, Anna Nikiforovskaya, Felix Schrimper, Ljube Boskovski, Oleg Suzdalev, Vsevolod Konyakhin

Project Augmented Reality


Project Description: The Zeiss PiWeb Zeiss enables metrologists and quality managers to create key insights and can promote product quality and productivity. The system offers real-time intuition on what is happening on the production facilities and helps to reduce the risk of downtime due to predictive maintenance approaches and enables better processes that result in higher quality at lower costs. The developed application creates a shared AR view for multiple metrologists using across various platforms. Using AR, metrologists can use a Microsoft Hololens, iOS device, and Android device to analyze the same part with the measurement points projected on the part.

Customers: Sabrina Senna

Project Leader: Paul Schmiedmayer

Developers: Andi Turdiu, Evgeny Motorin, Johannes Rohwer, Nadya Bugakova, Natasha Murashkina, Nicolas Neudeck, Sandra Grujovic, Sebastian Aigner

Project Predictive Maintenance


Project Description: Predictive maintenance techniques are used to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises higher availability and costs savings over traditional maintenance management. To use predictive maintenance, the data scientists at ZEISS need to extract valuable information from sensor data applying state-of-the-art machine learning methods to ensure that the provided information add true user value. For reliable machine learning models sensor data must be carefully labeled. However, the process of data labeling is a highly time-consuming task requiring domain expertise. Data scientists need to consult domain experts to make sure data can be correctly labeled.

The aim of this project was to develop an application that provides the visualization of time-series data together with highlighting the possible anomalies, which come from unsupervised anomaly detection methods. The data scientist can accept or reject each of the propositions and in case of uncertainty, write a comment and ask the experts for their opinion.

Customers: Dr. Lydia Nemec, Kay-Uwe Clemens

Project Leader: Sajjad Taheri

Developers: Palle Klewitz, Nils Faulhaber, Vsevolod Stepanov, Aleksandr Karavaev


Miscellaneous JASS 2019 Impressions