diplomsko delo
Jores Grubar (Author), Luka Šajn (Mentor)

Abstract

V diplomskem delu predstavljamo pristop avtomatskega testiranja uporabniškega grafičnega vmesnika s pomočjo nevronskih mrež. Pokazati želimo, da lahko razvijemo sistem, ki bo na podlagi pridobljenih zaporedij uporabniških akcij s pomočjo generativnega napovednega modela zmožen generirati testne primere, ki bodo posnemali vedenje pravih uporabnikov, ter le-te nato samodejno izvesti. Pristopi, ki temeljijo na strojnem učenju uporabniških vzorcev programa zavoljo generiranja testnih primerov za testiranje grafičnega vmesnika, lahko odpravijo potrebo po ročnem pisanju testov hkrati pa izboljšajo pokritost testiranja. Na ta način izboljšamo stabilnost testirane programske opreme v produkcijskem okolju in sočasno razbremenimo človeške vire na tem področju. Osredotočamo se na uporabo rekurentnih nevronskih mrež, saj so le-te primeren in učinkovit način za modeliranje medsebojno odvisnih zaporednih podatkov.

Keywords

avtomatsko testiranje;nevronske mreže;simulacija uporabnika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [J. Grubar]
UDC: 004.5:004.85(043.2)
COBISS: 223449603 Link will open in a new window
Views: 38
Downloads: 8
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Other data

Secondary language: English
Secondary title: Automated graphical user interface testing using neural networks
Secondary abstract: The thesis explores an approach to automated graphical user interface testing through the application of neural networks. The primary objective is to show that we can develop a system capable of generating test cases based on sequences of user actions, which will imitate the behavior of real users, and can automatically execute them later on. Machine learning techniques for modeling user behavior patterns to generate graphical user interface test cases offer the potential to eliminate the need for manual test creation while enhancing test coverage. By adopting such approaches, the stability of the tested software in production environments can be improved, and human resources allocated to this task can be significantly reduced. The research focuses on recurrent neural networks due to their suitability for our domain, as they are specifically designed to model interdependent sequential data.
Secondary keywords: automated testing;neural networks;graphical user interface;machine learning;user simulation;computer and information science;diploma;Grafični uporabniški vmesniki;Nevronske mreže (računalništvo);Strojno učenje;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 1 spletni vir (1 datoteka PDF (49 str.))
ID: 25722888