Lokalizovanie zmien výkonnosti v kóde

Cieľom tejto práce je vylepšiť hľadanie zmien v zdrojovom kóde, ktoré možu sposobovať zmenu výkonnosti a uľahčiť tak testovanie software. Pre tento účel je vytvorený framework, ktorý obsahuje popis prípadne vytvorenie nástrojov a definíciu postupov ako s nimi pracovať. Práca začína s popisom profilovania a kým sposobom može tento proces ovplyvniť výsledky merania výkonnosti. Sú definované požiadavky na profiler potrebný na túto prácu a na ich základe bol zvolený OProfile ako vzorový profiler. Ďalšia časť obsahuje analýzu behu programu popis jeho dvoch častí Execution a Waiting. Nasleduje rozdelenie zmien v zdrojovóm kóde do jednotlivých kategórií podľa toho, akým sposobom je možné ich detekovať. Tretia časť definuje dve metódy na lokalizovanie zmien vo výkonnosti: filtrovanie zmien v kóde a porovnávanie výsledkov profileru. Filtrovanie zmien v kóde je definované ako prienik zoznamu zmien v zdrojovom kóde a zoznamu riadkov kódu, ktoré boli pri meraní spustené. Druhá metóda je len porovnávanie výstupov z profileru s použitím vizualizačného nástroja. Záverečná časť je ukážka použitia týchto metód v realnych projektochThe objective of this work is to improve look up for changes in source code performance and help to remove burden at software testing with it. It tries to design some framework for this purpose which includes creating or desribing tools and defining methods how to work with them. The work starts with desciption of profiling and how this process can influence the performance measurement results. The profiler requirements are defined for purposes of this work and the OProfile is selected as the representative profiler. The next part contains analysis of program run and description of Execution and Waiting part of the run. Following is the categorization of various source code changes due to the posibility of their detection. The third part defines two methods for locating performance regression: advanced difference filtering and comparing the profiler results. Advanced difference filtering is an intersection between a list of changed code parts and a list of code parts executed at measurement. The second method is just comparing the profiler output using the visualization tool. The final part is demonstration of these two methods on real projects.Katedra softwarového inženýrstvíDepartment of Software EngineeringFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

The invasive ‘mothcatcher’ (Araujia sericifera Brot.; Asclepiadoideae) co-opts native honeybees as its primary pollinator in South Africa

Araujia sericifera is a South American milkweed which is an invasive/exotic species in South Africa. This species relies on pollinators for sexual reproduction and we show it has co-opted native South African honeybees as primary pollinators in its adopted country. Moths also visit the flowers of this species, but contribute little to its relatively high pollination success

Locating Performance Regressions In Code

The objective of this work is to improve look up for changes in source code performance and help to remove burden at software testing with it. It tries to design some framework for this purpose which includes creating or desribing tools and defining methods how to work with them. The work starts with desciption of profiling and how this process can influence the performance measurement results. The profiler requirements are defined for purposes of this work and the OProfile is selected as the representative profiler. The next part contains analysis of program run and description of Execution and Waiting part of the run. Following is the categorization of various source code changes due to the posibility of their detection. The third part defines two methods for locating performance regression: advanced difference filtering and comparing the profiler results. Advanced difference filtering is an intersection between a list of changed code parts and a list of code parts executed at measurement. The second method is just comparing the profiler output using the visualization tool. The final part is demonstration of these two methods on real projects

Locating Performance Regressions In Code

The objective of this work is to improve look up for changes in source code performance and help to remove burden at software testing with it. It tries to design some framework for this purpose which includes creating or desribing tools and defining methods how to work with them. The work starts with desciption of profiling and how this process can influence the performance measurement results. The profiler requirements are defined for purposes of this work and the OProfile is selected as the representative profiler. The next part contains analysis of program run and description of Execution and Waiting part of the run. Following is the categorization of various source code changes due to the posibility of their detection. The third part defines two methods for locating performance regression: advanced difference filtering and comparing the profiler results. Advanced difference filtering is an intersection between a list of changed code parts and a list of code parts executed at measurement. The second method is just comparing the profiler output using the visualization tool. The final part is demonstration of these two methods on real projects