Performance Testing Strategy for Digital Enterprise Platforms

Akshaya Choudhary

Digital technologies have enveloped our lives like never before. Be it on the personal or professional front, we have become dependent on the accurate functioning of digital devices and the software running them. To cite a few examples to drive home the point – imagine going to an unknown city and depending on the GPS for guidance, using your digital wallet to make a payment, booking a movie or airlines ticket, or checking the balance of your bank account. Now imagine these activities coming to a standstill because the software running them has glitches. Hence, the performance of the software is critical in running the components and levers of the new digital ecosystem. And to ensure our digital ecosystem delivers the required outcomes, a robust performance testing strategy should be instituted.


What is Performance Testing?

It is about evaluating the performance of a system in terms of parameters like responsiveness and stability when the system is subjected to a specific load. Any application performance testing exercise checks for speed, reliability, robustness, and the size of the application. The performance indicators taken into consideration are:

■ Acceptable user traffic.

■ Response times of servers, networks, webpages, or browsers.

■ Memory processing time and consumption.

■ Type and frequency of errors or glitches encountered in the system.

Why Should the Performance of a System Be Tested?

Any system should be expected to deliver a superior user experience irrespective of the level of traffic it encounters. It should ideally meet the expected outcomes conceived at the time of its design. The performance of a system is critical to its adoption and success in the market, especially when the end-customers are flooded with new choices. Of late, the digital ecosystem is bursting at the seams with companies or app developers releasing new software systems at the drop of the hat. However, not every such software is lapped up by the end-customers.

Their success is predominantly underpinned on factors like performance, cost, security, usability, reliability, and stability. Also, there are times when a system is subjected to excessive load, say like the Black Friday sales. During such times, the system can be overwhelmed leading to its malfunctioning or even crashing. To prevent such a situation and to find out the threshold up to which the system can deliver outcomes consistently and of acceptable quality, a proper performance testing strategy should be drawn.

Further, in the absence of application performance testing, the responsiveness of a system during load times will remain under a cloud. This can be detrimental to the bottom line of organizations as bad user experiences can lead people to leave the application altogether. Also, in a broader perspective, the well-being of people can be at stake. For example, a healthcare management system can come under stress in situations of emergencies when prompt response is needed.

What Are the Benefits of Performance Testing?

By incorporating a robust performance testing approach in the SDLC, organizations can achieve the below-mentioned benefits.

■ Identifies the vulnerabilities or glitches in the system that make the latter work less efficiently.

■ Validates the features and functionalities of the software.

■ Enhances the capability of the system to handle the load.

■ Measures the stability, throughput, responsiveness, accuracy, and speed of the software.

■ Ensures the user-experience remains superior.

What Are the Different Types of Performance Testing?

The key testing types are related to load, unit, spike, soak, and stress.

■ Load testing is about simulating the number of virtual users and then check the application's response time. It helps to identify the resident glitches or bottlenecks preventing the application to perform to its optimum.

■ Unit testing is about simulating the transactional activity to identify and isolate transactions that could disrupt the functioning of the application.

■ Stress testing is about evaluating application behavior when subjected to peak loads.

■ Soak testing is about increasing the number of concurrent users and studying the behavior of the application over a period. It tests if sustained activity over a period can lead to a drop in the application's performance.

■ Spike testing is about studying the operation of systems when the load activity is above average.

What Should Be the Right Performance Testing Strategy?

It is only by taking the right performance testing approach that a system can generate the expected outcomes even when subjected to excessive loads.

■ Test early and test often to identify and fix glitches early in the development phase.

■ Follow the DevOps route of integrating the development and operations functions and incorporating performance engineering into the value chain.

■ Not only should the response of the servers be tested when subjected to excessive levels of load but focus on the actual users as well. Performance testing should be driven from the perspective of a single user to understand his or her experience.

■ Build a performance model to understand the system's capacity. It will help to plan the state related to concurrent users, simultaneous requests, server utilization, system throughput, and average user sessions. The model should define the KPIs, average latency, system throughput, CPU usage, network traffic, and hardware metrics, among others.

■ Define baseline performance measurements to choose the right testing goals concerning each environment. These can include the user login time and system response time, among others.

■ The testing should generate accurate reports to analyze the behavior of the system. The reports and data can help testers to pinpoint the causes of system malfunction during excessive load sessions.

Conclusion

Testing the performance of a system is important to ensure its eventual adoption and success in the market. It lets developers and testers to understand the load thresholds beyond which the system may not respond along expected lines. The information can help to fix the system's architecture and enhance its output.

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Performance Testing Strategy for Digital Enterprise Platforms

Akshaya Choudhary

Digital technologies have enveloped our lives like never before. Be it on the personal or professional front, we have become dependent on the accurate functioning of digital devices and the software running them. To cite a few examples to drive home the point – imagine going to an unknown city and depending on the GPS for guidance, using your digital wallet to make a payment, booking a movie or airlines ticket, or checking the balance of your bank account. Now imagine these activities coming to a standstill because the software running them has glitches. Hence, the performance of the software is critical in running the components and levers of the new digital ecosystem. And to ensure our digital ecosystem delivers the required outcomes, a robust performance testing strategy should be instituted.


What is Performance Testing?

It is about evaluating the performance of a system in terms of parameters like responsiveness and stability when the system is subjected to a specific load. Any application performance testing exercise checks for speed, reliability, robustness, and the size of the application. The performance indicators taken into consideration are:

■ Acceptable user traffic.

■ Response times of servers, networks, webpages, or browsers.

■ Memory processing time and consumption.

■ Type and frequency of errors or glitches encountered in the system.

Why Should the Performance of a System Be Tested?

Any system should be expected to deliver a superior user experience irrespective of the level of traffic it encounters. It should ideally meet the expected outcomes conceived at the time of its design. The performance of a system is critical to its adoption and success in the market, especially when the end-customers are flooded with new choices. Of late, the digital ecosystem is bursting at the seams with companies or app developers releasing new software systems at the drop of the hat. However, not every such software is lapped up by the end-customers.

Their success is predominantly underpinned on factors like performance, cost, security, usability, reliability, and stability. Also, there are times when a system is subjected to excessive load, say like the Black Friday sales. During such times, the system can be overwhelmed leading to its malfunctioning or even crashing. To prevent such a situation and to find out the threshold up to which the system can deliver outcomes consistently and of acceptable quality, a proper performance testing strategy should be drawn.

Further, in the absence of application performance testing, the responsiveness of a system during load times will remain under a cloud. This can be detrimental to the bottom line of organizations as bad user experiences can lead people to leave the application altogether. Also, in a broader perspective, the well-being of people can be at stake. For example, a healthcare management system can come under stress in situations of emergencies when prompt response is needed.

What Are the Benefits of Performance Testing?

By incorporating a robust performance testing approach in the SDLC, organizations can achieve the below-mentioned benefits.

■ Identifies the vulnerabilities or glitches in the system that make the latter work less efficiently.

■ Validates the features and functionalities of the software.

■ Enhances the capability of the system to handle the load.

■ Measures the stability, throughput, responsiveness, accuracy, and speed of the software.

■ Ensures the user-experience remains superior.

What Are the Different Types of Performance Testing?

The key testing types are related to load, unit, spike, soak, and stress.

■ Load testing is about simulating the number of virtual users and then check the application's response time. It helps to identify the resident glitches or bottlenecks preventing the application to perform to its optimum.

■ Unit testing is about simulating the transactional activity to identify and isolate transactions that could disrupt the functioning of the application.

■ Stress testing is about evaluating application behavior when subjected to peak loads.

■ Soak testing is about increasing the number of concurrent users and studying the behavior of the application over a period. It tests if sustained activity over a period can lead to a drop in the application's performance.

■ Spike testing is about studying the operation of systems when the load activity is above average.

What Should Be the Right Performance Testing Strategy?

It is only by taking the right performance testing approach that a system can generate the expected outcomes even when subjected to excessive loads.

■ Test early and test often to identify and fix glitches early in the development phase.

■ Follow the DevOps route of integrating the development and operations functions and incorporating performance engineering into the value chain.

■ Not only should the response of the servers be tested when subjected to excessive levels of load but focus on the actual users as well. Performance testing should be driven from the perspective of a single user to understand his or her experience.

■ Build a performance model to understand the system's capacity. It will help to plan the state related to concurrent users, simultaneous requests, server utilization, system throughput, and average user sessions. The model should define the KPIs, average latency, system throughput, CPU usage, network traffic, and hardware metrics, among others.

■ Define baseline performance measurements to choose the right testing goals concerning each environment. These can include the user login time and system response time, among others.

■ The testing should generate accurate reports to analyze the behavior of the system. The reports and data can help testers to pinpoint the causes of system malfunction during excessive load sessions.

Conclusion

Testing the performance of a system is important to ensure its eventual adoption and success in the market. It lets developers and testers to understand the load thresholds beyond which the system may not respond along expected lines. The information can help to fix the system's architecture and enhance its output.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...