Master Thesis MSTR-2020-51

BibliographyStadelmaier, Niko: Using software-performance-antipatterns and profiling traces to perform code-refactorings.
University of Stuttgart, Faculty of Computer Science, Master Thesis No. 51 (2020).
104 pages, english.

Today, usability, user satisfaction, as well as enterprise adoption of a software application, are highly influenced by the performance of the software application. Therefore, it is required to resolve performance issues as early as possible during the development of the software. Many issues can be resolved during the planning and design phase by integrating a model-based antipattern detection. Such approaches can be easily integrated with continuous development and integration pipelines, which are often used in modern software development following an agile development methodology. The focus of this thesis is to develop an approach that can automatically detect performance antipatterns and suggest refactorings for the found problems. In contrast to model-based approaches, the intention is to detect the problems on the code-level. To tackle the problem, we make use of profiling traces that record the execution of an application. After the initial research on antipatterns in Go, we introduce the identified code-based antipatterns. We then present the benchmark application, where we implemented the problems. This benchmark is then used to generate the profile traces. Now, we analyze how the problems can be detected in the profiles. We then extract our novel code- and profile-patterns from the profiling information. These patterns are then used by our detection tool to identify the problems in the profiles and suggest the respective refactorings. Our results show that our approach can automatically detect performance antipatterns in the profiling data. However, more tests need to be conducted to conclude if the approach can detect antipatterns in the data of other systems.

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Department(s)University of Stuttgart, Institute of Computer Science, Software Quality and Architecture
Superviser(s)van Hoorn, Dr. Andre; Okanovic, Dr. Dusan; Trubiani, Dr. Catia
Entry dateMarch 3, 2021
   Publ. Computer Science