Students

Vojtěch Hájek: Performance Analysis of C# Programs

Disclaimer: This thesis was mainly supervised by Ing. Jiří Pavela; I have served as additional technical consultant. The goal of this thesis was to extend our profilers with profiler for programs written in C#, potentially extending our support for Windows (sub)systems. The results were interpreted using tree-like views similar to outputs of kcachegrind.

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Šimon Stupinský: New Models for Automatic Detection of Performance Degradation

Disclaimer: While this thesis was officially supervised by Adam Rogalewicz (due some internal restrictions), I was the main supervisor of this whole thesis. The goal of this thesis was to extend the capabilities of Perun with new models (besides so-far supported regression analysis) as well as new approaches to detect performance degradations in software. In particular, we come up with non-parametric models (models not dependent on other variables) such as regressograms, moving averages or kernel regressions; as well as methods for detecting performance issues based on integral computation or local statistics.

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Ondřej Pavela: Static Analysis Using Facebook Infer Focused on Performance Analysis

Disclaimer: This thesis was supervised by Tomáš Vojnar, I was the technical consultant. The goal of this thesis was build upon our work on automatic complexity analysis; this was initial work that only had to reproduce the results of Loopus tools invented by our colleagues in TU Vienna (Moritz Sinn and Florian Zuleger) in the Facebook Infer framework.

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Matúš Liščinský: Fuzz Testing of Program Performance

Disclaimer: While this thesis was officially supervised by Adam Rogalewicz (due some internal restrictions), I was the main supervisor of this whole thesis. The goal of this thesis was to extend the notion of performance fuzztesting to more fine-tuned (custom) rules and more performance-based evaluation. The resulting fuzzer is part of Perun and provides new ideas (sadly unpublished).

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Jiří Pavela: Library for Profiling of Data Structures of C/C++ Programs

The goal of this thesis was to create a time profiler of C/C++ programs together with tracking sizes of advanced data structures (such as linked lists or skip lists). The collected data was postprocessed using regression analysis to infer mathematical models (which modeled runtime of functions based on sizes of underlying structures). The results were visualized using scatter plots.

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