The client is a significant provider of innovative solutions in the e-learning business. They have changed the learning experience by delivering digital information services, course–driven training programmes, and content for instructors, students, and other academic bodies. This change has made it easier for them to support and improve higher education’s fundamentals: knowledge, teaching, learning, research, and service.

Business Objectives and Needs

The problem with this large-scale online e-learning system was achieving and maintaining its mission-critical application at peak performance and scalability levels. The application capacity was also an unknown danger because the system lacked any reliable, well-framed approach for predicting system behaviour and performance under real-time stress.

While attempting to put up a proper environment and tests, the software ran into a variety of challenges, including:

Bulk test data – It takes a lot of planning, organisation, and data preparation to create the test data needed to replicate a huge number of login IDs. <the company>devised a system to create over 100,000 student accounts, 2,000 professors, and over 2,000 classes. Each lesson has many sessions, various types of content, and multiple homework assignments.

Using the right tool — We have used one sort of tool to generate the static background load on the system, and another type of tool to handle complicated scripts. The client saved money by not having to purchase any more load generator licences to handle all of the traffic.

Comparable Findings Evaluation – It’s tough to compare results when the database size of the application under test fluctuates across test cycles. Comparing the results of a 200-student class to a 1500-student class is likewise ludicrous. Because the number of courses in the database was expected to grow rapidly, <company name> and the customer met to discuss the expansion rate and the archiving process.

Scalability, performance, and usefulness under high loads, as well as a thorough data privacy guarantee, were the client’s major concerns.

• Identification and development of a detailed grasp of the client’s application and system architecture.
• Identify areas of performance risk, performance targets, and needs to establish adequate focus and avoid costly over- or under-testing. 
• Load tests are uniform, measurable, and repeatable thanks to the use of scripts and scenarios. 
• We have also included performance tuning advice aimed at reducing response times and eliminating resource bottlenecks.
• Trend analysis to identify potential scalability issues that may affect consumers in the future. Test scripts, data, results, and environment configurations, as well as documentation of our testing approach, are all included.
• Simulated components with stand-alone capability. 
• Performance Unit Tests are created after a comprehensive examination of the programme in development.
• SilkPerformer, JMeter, and standalone Java code are all used.

Some of the benefits that emerged as a result of these tests are as follows:

• Several concerns with the code’s performance were effectively handled. This resulted from strong collaboration with development teams.
• Implementing cache servers in the deployment model, as well as determining the best configuration of various hardware server components.
• Load testing took place at various stages of the agile development process. Each individual function was tested as it became available, and each class type was tested as it was constructed, with performance measurements provided for each deployment. Due to continuous improvement and meeting milestones, the summer and spring updates were released on time and without difficulties.

Summary –

Database transaction rates and Java memory management were thoroughly scrutinised in order to fulfil the desired response times. Significant improvements in performance, cost savings, and end-user satisfaction resulted from extensive redesigning of the application, database schema, and production settings.