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Performance Testing of a Banking Application using JMeter

about client

The client is a leading application development and application service provider (ASP) in the domain of banking and finance.

The application is a finance tracking application in which client wants to manage all his customer’s financial activities and updates on the web and mobile platforms.

The client hired us to identify the application’s performance under diverse conditions and the behavior of the system on increasing possible number of users in the future.

Product Challenges

01

It was hard to get each test’s threshold even after a number of iterations.

02

Ajax requests and the scenarios in which we have to create scripts were quite challenging.

03

Identify or predict the performance and bottlenecks that need to improve for expected load conditions.

04

Running an iteration and identify next iteration’s requirements based on different parameter was also a big challenge.

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Product Solutions

01

Created a test strategy based on the client requirement with a custom framework.

02

We used the Test Script Recorder to trace Ajax request in the application and also used JUnit scripts by integrating WebDriver.

03

We used the Test Script Recorder to trace Ajax request in the application and also used JUnit scripts by integrating WebDriver.

04

Used “PerfMon Server Agent” to monitor server resource utilization e.g. CPU, RAM, Disk I/O and Network I/O.

05

Generated a custom test execution report that provides a simple and clear view of each iteration results.

Tools And Technology

  • Junit:: A unit testing framework for testing Java-based application.
  • Jmeter: A powerful Load Testing Tool that can analyze the performance of an application through automated tests.
  • Test Script Recorder
  • PerfMon Server Agent
  • Custom framework
  • WebDriver: Automated testing framework for performance testing across different web browsers.

Client Benefits

  • Reduced risk and overall time for testing life cycle.
  • Response time improved by 70%.
  • Identified production server requirement based on performance data.
  • Time and cost saving by performing the test under tight deadlines.
  • Representation of the response time, throughput, possible error percentage and server resource utilization of current infrastructure.

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