A Kafka-Based Robot Automation Testing Using Genetic Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587818" target="_blank" >RIV/70883521:28140/24:63587818 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-99-8703-0_25" target="_blank" >http://dx.doi.org/10.1007/978-981-99-8703-0_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-99-8703-0_25" target="_blank" >10.1007/978-981-99-8703-0_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Kafka-Based Robot Automation Testing Using Genetic Algorithm

Popis výsledku v původním jazyce

The demand for real-time processing of high-volume data streams in contemporary applications is increasing exponentially. Typical application areas include the maintenance of IoT devices, fraud detection systems, electronic trading platforms, etc.… For software development teams (DevOPs), scaling software test automation while managing the test automation process within a reasonable time continues to be a major difficulty. Among the numerous software testing frameworks available, the Data-Driven Testing Framework (DDTF) and the Test-Driven Framework Development are the most popular. Due to the aforementioned constraints, this study utilizes Kafka middleware, a distributed messaging system used in contemporary stream-based applications with reliable and effective stream delivery capacity. Our work primarily uses three tools: Kafka, Robot Framework for Automation Testing (RFAT), and Genetic Algorithm (GA). GA is used to create the list of test cases before the deployment of the Kafka-based implementation of robot automation software testing. We select the first endpoint from the four available endpoints to provide data based on the population size and the given number of generations. The second endpoint (producer) will be called by the RFAT which then forwards the data to the Kafka server in the form of a topic. Finally, the third endpoint consumes the topic-based data from the Kafka server and sends it back to the RFAT where the fourth endpoint will be utilized. To test the average and maximum fitness values, we retrieve generations in accordance with a given threshold value after calling the fourth endpoint.

Název v anglickém jazyce

A Kafka-Based Robot Automation Testing Using Genetic Algorithm

Popis výsledku anglicky

The demand for real-time processing of high-volume data streams in contemporary applications is increasing exponentially. Typical application areas include the maintenance of IoT devices, fraud detection systems, electronic trading platforms, etc.… For software development teams (DevOPs), scaling software test automation while managing the test automation process within a reasonable time continues to be a major difficulty. Among the numerous software testing frameworks available, the Data-Driven Testing Framework (DDTF) and the Test-Driven Framework Development are the most popular. Due to the aforementioned constraints, this study utilizes Kafka middleware, a distributed messaging system used in contemporary stream-based applications with reliable and effective stream delivery capacity. Our work primarily uses three tools: Kafka, Robot Framework for Automation Testing (RFAT), and Genetic Algorithm (GA). GA is used to create the list of test cases before the deployment of the Kafka-based implementation of robot automation software testing. We select the first endpoint from the four available endpoints to provide data based on the population size and the given number of generations. The second endpoint (producer) will be called by the RFAT which then forwards the data to the Kafka server in the form of a topic. Finally, the third endpoint consumes the topic-based data from the Kafka server and sends it back to the RFAT where the fourth endpoint will be utilized. To test the average and maximum fitness values, we retrieve generations in accordance with a given threshold value after calling the fourth endpoint.

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Electrical Engineering

ISBN

978-981-9987-02-3

ISSN

1876-1100

e-ISSN

1876-1119

Počet stran výsledku

12

Strana od-do

297-308

Název nakladatele

SPRINGER-VERLAG SINGAPORE PTE LTD

Místo vydání

SINGAPORE

Místo konání akce

Ho Chi Minh City

Datum konání akce

8. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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