International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 11, Issue 9 (September 2024), Pages: 214-226

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 Original Research Paper

Web application performance assessment: A study of responsiveness, throughput, and scalability

 Author(s): 

 Hend Alnuhait 1, Wael Alzyadat 2, Ahmad Althunibat 2, Hasan Kahtan 3, Belal Zaqaibeh 4, Haneen A. Al-Khawaja 5, 6, 7, *

 Affiliation(s):

 1Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia
 2Faculty of Sciences and Information Technology, Al-Zaytoonah University of Jordan, Amman, Jordan
 3Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales
 4Faculty of Science and Information Technology, Jadara University, Irbid, Jordan
 5Department of Financial Technology and Banking, Faculty of Business, Ajloun National University, Ajloun, Jordan
 6Applied Science Research Center, Applied Science Private University, Amman, Jordan
 7Swiss FinTech Innovation Lab, University of Zurich, Zurich, Switzerland

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4607-9394

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.09.023

 Abstract

This study examines web application performance testing by focusing on responsiveness, throughput, and scalability to evaluate the effectiveness of computer systems, networks, and software applications. It assesses a specific protocol's performance through four tests: performance load, process start-up time, web application infrastructure, and resource allocation. Using Apache JMeter, tests were conducted on the RSMD and E-government websites. The results revealed instability and performance degradation in the RSMD website over time, with server-to-client response time increasing as the test duration and load increased. The E-GOV website's performance initially appeared stable but also degraded over time. A test ramp time of 10 seconds and five looping iterations showed significant performance degradation. Future research should address these issues to improve web application performance under load conditions. The study also discusses testing tools, including JMeter, for evaluating website performance under various load conditions. Key findings include the instability of the RSMD website and the performance deterioration of the E-GOV website, especially in scenarios with a 10-second ramp time and five loop iterations. These insights provide valuable guidance for developing strategies to optimize website performance under high-traffic conditions.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Web application performance testing, Responsiveness, Throughput, Scalability, Performance degradation

 Article history

 Received 1 April 2024, Received in revised form 1 August 2024, Accepted 14 September 2024

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alnuhait H, Alzyadat W, Althunibat A, Kahtan H, Zaqaibeh B, and Al-Khawaja HA (2024). Web application performance assessment: A study of responsiveness, throughput, and scalability. International Journal of Advanced and Applied Sciences, 11(9): 214-226

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 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

 Table 1 Table 2 Table 3 

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 References (35)

  1. Aazam M, Zeadally S, and Harras KA (2018). Fog computing architecture, evaluation, and future research directions. IEEE Communications Magazine, 56(5): 46-52. https://doi.org/10.1109/MCOM.2018.1700707   [Google Scholar]
  2. Abbas R, Sultan Z, and Bhatti SN (2017). Comparative analysis of automated load testing tools: Apache JMeter, Microsoft Visual Studio (TFS), LoadRunner, Siege. In the International Conference on Communication Technologies (ComTech): 39-44. IEEE, Rawalpindi, Pakistan. https://doi.org/10.1109/COMTECH.2017.8065747   [Google Scholar]
  3. Agnihotri J and Phalnikar R (2018). Development of performance testing suite using Apache JMeter. In: Bhalla S, Bhateja V, Chandavale A, Hiwale A, and Satapathy S (Eds.), Intelligent computing and information and communication: Advances in intelligent systems and computing: 317-326. Volume 673, Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-10-7245-1_32   [Google Scholar]
  4. Al Houl MAA, Alqudah MTS, Almomani MAA, and Eid QMA (2023). The risks of financial derivatives and alternatives from the viewpoint of Islamic economics. International Journal of Professional Business Review, 8(4): e01213. https://doi.org/10.26668/businessreview/2023.v8i4.1213   [Google Scholar]
  5. Alhroob A, Alzyadat W, Imam AT, and Jaradat GM (2020). The genetic algorithm and binary search technique in the program path coverage for improving software testing using big data. Intelligent Automation and Soft Computing, 26(4): 725–733. https://doi.org/10.32604/iasc.2020.010106   [Google Scholar]
  6. Alhyari S, Alazab M, Venkatraman S, Alazab M, and Alazab A (2013). Performance evaluation of e‐government services using balanced scorecard: An empirical study in Jordan. Benchmarking: An International Journal, 20(4): 512-536. https://doi.org/10.1108/BIJ-08-2011-0063   [Google Scholar]
  7. Almaiah MA, Al-Khasawneh A, Althunibat A, and Khawatreh S (2020). Mobile government adoption model based on combining GAM and UTAUT to explain factors according to adoption of mobile government services. International Journal of Interactive Mobile Technologies, 14(3): 199-225. https://doi.org/10.3991/ijim.v14i03.11264   [Google Scholar]
  8. Alshehadeh AR and Al-Khawaja HA (2022). Financial technology as a basis for financial inclusion and its impact on profitability: Evidence from commercial banks. International Journal of Advances in Soft Computing and Its Applications, 14(2): 125-138. https://doi.org/10.15849/IJASCA.220720.09   [Google Scholar]
  9. Al-Tamimi KAM, Jaradat MS, YachouAityassine FL, and Soumadi MM (2023). Impact of renewable energy on the economy of Saudi Arabia. International Journal of Energy Economics and Policy, 13(3): 20-27. https://doi.org/10.32479/ijeep.14099   [Google Scholar]
  10. Althunibat A (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52: 65-71. https://doi.org/10.1016/j.chb.2015.05.046   [Google Scholar]
  11. Althunibat A, Abdallah M, Almaiah MA, Alabwaini N, and Alrawashdeh TA (2022). An acceptance model of using mobile-government services (AMGS). CMES-Computer Modeling in Engineering and Sciences, 131(2): 865-880. https://doi.org/10.32604/cmes.2022.019075   [Google Scholar]
  12. Althunibat A, AlNuhait H, Almanasra S, Almajali MH, Aljarrah E, and Al-Khawaja HA (2024). Culture and law enforcement influence on m-government adoption: An exploratory study. Journal of Infrastructure, Policy and Development, 8(5): 3353. https://doi.org/10.24294/jipd.v8i5.3353   [Google Scholar]
  13. Althunibat A, Alokush B, Dawood R, Tarabieh SM, and Gil-Pechuan I (2021b). Modeling the factors that influence digital economy services acceptance. In the International Conference on Information Technology, IEEE, Amman, Jordan: 942-945. https://doi.org/10.1109/ICIT52682.2021.9491686   [Google Scholar]
  14. Althunibat A, Alokush B, Tarabieh SM, and Dawood R (2021c). Mobile government and digital economy relationship and challenges. International Journal of Advances in Soft Computing and Its Applications, 13(1): 122-134.   [Google Scholar]
  15. Althunibat A, Alrawashdeh TA, and Muhairat M (2014). The acceptance of using m-government services in Jordan. In the 11th International Conference on Information Technology: New Generations, IEEE, Las Vegas, USA: 643-644. https://doi.org/10.1109/ITNG.2014.65   [Google Scholar]
  16. Althunibat A, Binsawad M, Almaiah MA, Almomani O, Alsaaidah A, Al-Rahmi W, and Seliaman ME (2021a). Sustainable applications of smart-government services: A model to understand smart-government adoption. Sustainability, 13(6): 3028. https://doi.org/10.3390/su13063028   [Google Scholar]
  17. Bora A, Medhi S, and Bezboruah T (2022). Reliability evaluation for deployment of multi service multi functional service oriented computing based on different techniques. International Journal of Advanced Intelligence Paradigms, 22(3-4): 362-378. https://doi.org/10.1504/IJAIP.2022.124319   [Google Scholar]
  18. Flores A, Ramírez S, Toasa R, Vargas J, Urvina-Barrionuevo R, and Lavin JM (2018). Performance evaluation of NoSQL and SQL queries in response time for the e-government. In the International Conference on eDemocracy and eGovernment, IEEE, Ambato, Ecuador: 257-262. https://doi.org/10.1109/ICEDEG.2018.8372362   [Google Scholar]
  19. Imam AT, Alhroob A, and Alzyadat WJ (2021). SVM machine learning classifier to automate the extraction of SRS elements. International Journal of Advanced Computer Science and Applications, 12(3): 174-185. https://doi.org/10.14569/IJACSA.2021.0120322   [Google Scholar]
  20. Iranpour E and Sharifian S (2018). A distributed load balancing and admission control algorithm based on Fuzzy type-2 and Game theory for large-scale SaaS cloud architectures. Future Generation Computer Systems, 86: 81-98. https://doi.org/10.1016/j.future.2018.03.045   [Google Scholar]
  21. Jebril I, Almaslmani R, Jarah B, Mugableh M, and Zaqeeba N (2023). The impact of strategic intelligence and asset management on enhancing competitive advantage: The mediating role of cybersecurity. Uncertain Supply Chain Management, 11(3): 1041-1046. https://doi.org/10.5267/j.uscm.2023.4.018   [Google Scholar]
  22. Kalita M and Bezboruah T (2012). Investigations on implementation of web applications with different techniques. IET Software, 6(6): 474-478. https://doi.org/10.1049/iet-sen.2011.0136   [Google Scholar]
  23. Karim A and Adnan MA (2019). An OpenID based authentication service mechanisms for Internet of Things. In the IEEE 4th International Conference on Computer and Communication Systems, IEEE, Singapore, Singapore: 687-692. https://doi.org/10.1109/CCOMS.2019.8821761   [Google Scholar]
  24. Mecca G, Santomauro M, Santoro D, and Veltri E (2016). On federated single sign-on in e-government interoperability frameworks. International Journal of Electronic Governance, 8(1): 6-21. https://doi.org/10.1504/IJEG.2016.076684   [Google Scholar]
  25. Milani Fard A, Mirzaaghaei M, and Mesbah A (2014). Leveraging existing tests in automated test generation for web applications. In the 29th ACM/IEEE International Conference on Automated Software Engineering, Association for Computing Machinery, Vasteras, Sweden: 67-78. https://doi.org/10.1145/2642937.2642991   [Google Scholar]
  26. Mumtaz R, Samawi V, Alhroob A, Alzyadat W, and Almukahel I (2022). PDIS: A service layer for privacy and detecting intrusions in cloud computing. International Journal of Advances in Soft Computing and Its Applications, 14(2): 14-34. https://doi.org/10.15849/IJASCA.220720.02   [Google Scholar]
  27. Pradeep S and Sharma YK (2019). A pragmatic evaluation of stress and performance testing technologies for web based applications. In the Amity International Conference on Artificial Intelligence, IEEE, Dubai, UAE: 399-403. https://doi.org/10.1109/AICAI.2019.8701327   [Google Scholar]
  28. Putri MA, Hadi HN, and Ramdani F (2017). Performance testing analysis on web application: Study case student admission web system. In the International Conference on Sustainable Information Engineering and Technology, IEEE, Malang, Indonesia: 1-5. https://doi.org/10.1109/SIET.2017.8304099   [Google Scholar]
  29. Saia SM, Nelson NG, Young SN, Parham S, and Vandegrift M (2022). Ten simple rules for researchers who want to develop web apps. PLOS Computational Biology, 18(1): e1009663. https://doi.org/10.1371/journal.pcbi.1009663   [Google Scholar] PMid:34990469 PMCid:PMC8735566
  30. Sedek KA, Omar MA, and Sulaiman S (2014). A hybrid architecture for one-stop e-government portal integration and interoperability. In the 8th Malaysian Software Engineering Conference (MySEC), IEEE, Langkawi, Malaysia: 96-101. https://doi.org/10.1109/MySec.2014.6985996   [Google Scholar]
  31. Shaw J (2000). Web application performance testing—A case study of an on-line learning application. BT Technology Journal, 18(2): 79-86. https://doi.org/10.1023/A:1026732502654   [Google Scholar]
  32. Staegemann D, Volk M, Lautenschläger E, Pohl M, Abdallah M, and Turowski K (2021). Applying test driven development in the big data domain–Lessons from the literature. In the International Conference on Information Technology, IEEE, Amman, Jordan: 511-516. https://doi.org/10.1109/ICIT52682.2021.9491728   [Google Scholar]
  33. Thatmann D, Slawik M, Zickau S, and Küpper A (2012). Towards a federated cloud ecosystem: Enabling managed cloud service consumption. In the 9th International Conference on Economics of Grids, Clouds, Systems, and Services, Springer, Berlin, Germany: 223-233. https://doi.org/10.1007/978-3-642-35194-5_17   [Google Scholar]
  34. Zeebaree SR, Jacksi K, and Zebari RR (2020). Impact analysis of SYN flood DDoS attack on HAProxy and NLB cluster-based web servers. Indonesian Journal of Electrical Engineering and Computer Science, 19(1): 510-517. https://doi.org/10.11591/ijeecs.v19.i1.pp505-512   [Google Scholar]
  35. Zou Z and Ai J (2020). Online prediction of server crash based on running data. In the 20th International Conference on Software Quality, Reliability and Security Companion, IEEE, Macau, China: 7-14. https://doi.org/10.1109/QRS-C51114.2020.00014   [Google Scholar]