Robot automation testing of software using genetic algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63574378" target="_blank" >RIV/70883521:28140/23:63574378 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10253052" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10253052</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICECCME57830.2023.10253052" target="_blank" >10.1109/ICECCME57830.2023.10253052</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robot automation testing of software using genetic algorithm

Popis výsledku v původním jazyce

The demand for excellent software has significantly increased in recent years, bringing the importance of testing-related challenges into the limelight. When generating test data for software testing, the test data must be able to unearth potential software defects, while the test adequacy criterion guarantees the quality of test cases. However, optimizing test data during software testing can improve software reliability. Recently, population-based metaheuristic search techniques (e.g., evolutionary testing) have been utilized in software testing. In this study, we propose and implement a method that utilizes a genetic algorithm to optimize test data for robot testing. Due to its advantages over traditional testing methods, several businesses have recently started using robot-automated testing systems for various applications. We implement a Robot Framework (R.F.) where we receive the data generated by a genetic algorithm. Furthermore, this generated data then acts as a request body for R.F. to test the fitness values and use the generated data as our necessary data sets.

Název v anglickém jazyce

Robot automation testing of software using genetic algorithm

Popis výsledku anglicky

The demand for excellent software has significantly increased in recent years, bringing the importance of testing-related challenges into the limelight. When generating test data for software testing, the test data must be able to unearth potential software defects, while the test adequacy criterion guarantees the quality of test cases. However, optimizing test data during software testing can improve software reliability. Recently, population-based metaheuristic search techniques (e.g., evolutionary testing) have been utilized in software testing. In this study, we propose and implement a method that utilizes a genetic algorithm to optimize test data for robot testing. Due to its advantages over traditional testing methods, several businesses have recently started using robot-automated testing systems for various applications. We implement a Robot Framework (R.F.) where we receive the data generated by a genetic algorithm. Furthermore, this generated data then acts as a request body for R.F. to test the fitness values and use the generated data as our necessary data sets.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

ISBN

979-8-3503-2298-9

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, New Jersey

Místo konání akce

Tenerife

Datum konání akce

19. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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