TY - GEN
T1 - A Recommendation Model of REST API Testing Framework based on Resource Utilization of ISO / IEC 25010
AU - Thooriqoh, Hazna At
AU - Rochimah, Siti
AU - Fatichah, Chastine
AU - Alfian, Muhammad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A recommendation model is needed to evaluate the REST API automated test framework based on its performance. In this study, the recommendation model has been built using the performance assessment of the testing framework based on the Resource Utilization aspect of ISO/IEC 25010. It evaluates on three types of REST API transactions: CRUD, upload file, and get massive data (GMD). In order to obtain the Resource Utilization scores, the resource usage from testing execution process of each selected test framework will be measured. Also, we need The weight value obtains from a questionnaire filled out by several experts to assess the importance of each RU aspect for each transaction type. The recommendation model will evaluate the testing frameworks as good, medium, or low. The model we propose can assess the framework well and is in line with the expectations of experts. This is evidenced by the high level of accuracy for CRUD and GMD transactions, which is 100%. However, file upload transactions show an accuracy rate of 87.5%. This is due to the incompatibility of data obtained from experts with data obtained from resource utilization. Futher research, we recommend to choose an expert who has mastered the seven frameworks well.
AB - A recommendation model is needed to evaluate the REST API automated test framework based on its performance. In this study, the recommendation model has been built using the performance assessment of the testing framework based on the Resource Utilization aspect of ISO/IEC 25010. It evaluates on three types of REST API transactions: CRUD, upload file, and get massive data (GMD). In order to obtain the Resource Utilization scores, the resource usage from testing execution process of each selected test framework will be measured. Also, we need The weight value obtains from a questionnaire filled out by several experts to assess the importance of each RU aspect for each transaction type. The recommendation model will evaluate the testing frameworks as good, medium, or low. The model we propose can assess the framework well and is in line with the expectations of experts. This is evidenced by the high level of accuracy for CRUD and GMD transactions, which is 100%. However, file upload transactions show an accuracy rate of 87.5%. This is due to the incompatibility of data obtained from experts with data obtained from resource utilization. Futher research, we recommend to choose an expert who has mastered the seven frameworks well.
KW - ISO/IEC 25010
KW - REST API automated testing framework
KW - Recommendation Model
KW - Resource Utilization
KW - Weight Coefficient
UR - http://www.scopus.com/inward/record.url?scp=85150192282&partnerID=8YFLogxK
U2 - 10.1109/ISRITI56927.2022.10052922
DO - 10.1109/ISRITI56927.2022.10052922
M3 - Conference contribution
AN - SCOPUS:85150192282
T3 - 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
SP - 383
EP - 389
BT - 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022
Y2 - 8 December 2022 through 9 December 2022
ER -