A Kafka-Based Robot Automation Testing Using Genetic Algorithm
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
A Kafka-Based Robot Automation Testing Using Genetic Algorithm
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Electrical Engineering
ISBN
978-981-9987-02-3
ISSN
1876-1100
e-ISSN
1876-1119
Number of pages
12
Pages from-to
297-308
Publisher name
SPRINGER-VERLAG SINGAPORE PTE LTD
Place of publication
SINGAPORE
Event location
Ho Chi Minh City
Event date
Dec 8, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—