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Title:Uporaba metod strojnega učenja za oblikovanje profila obremenitve spletnih aplikacij
Authors:ID Unuchak, Yauhen (Author)
ID Kljajić Borštnar, Mirjana (Mentor) More about this mentor... New window
Files:.pdf MAG_Unuchak_Yauhen_2024.pdf (8,02 MB)
MD5: F477FE63031555CFDD52DFAEAE172AC1
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:V magistrskem delu je predstavljen razvoj modelov gručenja s pomočjo programskega jezika Python. Cilj dela je razviti prototip orodij za oblikovanje profila obremenitve pri testiranju zmogljivosti spletnih aplikacij na podlagi analize preteklega delovanja spletne aplikacije v produkcijskem okolju. Izdelava profila obremenitve za testiranje zmogljivosti spletnih aplikacij predstavlja ključno fazo obremenitvenega testiranja. Ta faza omogoča usklajevanje testnih podatkov in obsega z zahtevami naročnika ter dejanskimi uporabniškimi izkušnjami. Pri izdelavi profila je treba upoštevati uporabniške vloge, tipične scenarije delovanja in razmerje med različnimi scenariji, saj različne funkcionalnosti aplikacije porabijo različne vire strežnika. Obremenitveno testiranje vključuje simulacijo uporabniških scenarijev s specializirano programsko opremo, kot sta JMeter in LoadRunner. To rešitev lahko uporabljajo inženirji in IT-strokovnjaki pri oblikovanju profila obremenitve, ki se ukvarjajo z obremenitvenim testiranjem zmogljivosti spletnih aplikacij. V delu raziskujemo, kako odkriti uporabniške vzorce za izboljšanje oblikovanja profila obremenitve za testiranje zmogljivosti na podlagi analize zapisov (rudarjenja podatkov iz log-datotek) o delovanju spletne aplikacije v produkcijskem okolju. S pravilno zasnovanim profilom obremenitve je mogoče oceniti zmogljivost in stabilnost sistema ter simulirati realne uporabniške pogoje.
Keywords:profil obremenitve spletnih aplikacij, testiranje, podatkovno rudarjenje spletnih zapisov
Place of publishing:Maribor
Year of publishing:2024
PID:20.500.12556/DKUM-89208 New window
COBISS.SI-ID:200822019 New window
Publication date in DKUM:05.07.2024
Views:287
Downloads:117
Metadata:XML DC-XML DC-RDF
Categories:FOV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:26.06.2024

Secondary language

Language:English
Title:Use of machine learning methods to create load profiles for web applications
Abstract:This master thesis presents the development of clustering models using the Python programming language. The aim of the thesis is to develop prototype tools for load profiling in performance testing of web applications based on the analysis of previous web application operation in a production environment. Creating a load profile for web application performance testing represents a crucial phase of load testing. This phase allows the test data and scope to be aligned with the client's requirements and the actual user experience. The profiling process needs to consider user roles, typical performance scenarios and the relationship between different scenarios, as different application functionalities consume different server resources. Load testing involves simulating user scenarios with specialized software such as JMeter and LoadRunner. This solution can be used by engineers and IT professionals when creating a load profile to deal with load testing the performance of web applications. In this work, we investigate how to detect user patterns in turn to improve the design of a load profile for performance testing based on the analysis of previous records (log-file data mining) of web application operation in a production environment. With a properly designed load profile, it is possible to assess the performance and stability of the system and to simulate realistic user operating scenarios.
Keywords:performance testing, workload user profile, web-log mining


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