Master Thesis MSTR-2025-120

BibliographyKlein, Johannes: Automatic Performance Problem Diagnostics for Embedded Software.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 120 (2025).
133 pages, english.
Abstract

Context: The reliability of an embedded system and its functionality is a crucial factor for the success of a product on the market. This reliability of functionality corresponds directly to the performance of the embedded software. Problem: Observing and resolving performance problems correlates with the point in time of the development process. The later the detection, the higher the cost [Ins]. Additionally, manual testing to observe these issues is often infeasible, considering a test during code integration. Objective: There exists a concept which enables the automatic performance problem diagnostics of run-time based enterprise software. The objective is to adapt this concept to be applicable to the domain of embedded software. Method: In order to adapt the concept, every aspect of it needs to be analyzed in context of embedded systems and software. The adaptation is evaluated in form of a case study, conducting different experiments with the iMOW®as test subject. Result: The concept is successfully adopted to the domain of embedded software, correctly detecting the presence and absence of performance problems of the iMOW®. Conclusion: Adapting the concept provides a new possibility to automatically diagnose the existence of performance problems of embedded software, which can be implemented to improve its development process.

Department(s)University of Stuttgart, Institute of Software Technology, Software Quality and Architecture
Superviser(s)Becker, Prof. Steffen
Entry dateAugust 13, 2026
   Publ. Institute   Publ. Computer Science