Improving application performance is undergoing significant change for the past few years. With the increase in workloads, datasets, and devices, performance engineering has become an essential part of development lifecycle. However, challenge in making performance engineering is also part of this process.

As applications are running in complex and dynamic environments, several factors like network conditions, user behavior, and lack of visibility can cause a cascading effect downstream (or upstream) derailing the application completely. Testing teams are often caught up in other testing techniques, leaving performance engineering behind.

Any performance engineering framework used correctly can provide significant advantages in improving application quality and performance, but when used incorrectly it can also massively amplify performance failures. These failures quickly propagate and lead to major brand image and revenue losses. And the history of the technology industry is filled with examples due to application performance failures.

In application economy, performance testing is one of the most discussed topics. We are here to help you understand the basics about performance testing and how to orchestrate it in your application.

So, without further ado, let’s get started –

What is Performance Testing?

Importance of Performance Testing

Objectives of Performance Testing

Types of Performance Testing

Challenges in Performance Testing

Performance Testing Workflow

Performance Metrics

Best Practices for Performance Testing

How Enhops can Help?

What is Performance Testing?

Simply put, Performance testing refers to testing your software/application/digital product under various load and traffic conditions. The goal of performance testing is to identify bottlenecks and performance issues at an early stage. The robust performance testing measures helps in validating the system scalability and measure response times under varying load and stress conditions. Performance testing covers data loads, servers, networks, databases, and user traffic.

Different types of performance testing include load testing, stress testing, endurance testing, spike testing, capacity testing, scalability testing, and volume testing.

Importance of Performance Testing

In 2011, the eCommerce giant Amazon experienced a significant performance failure (the accurate reason of the failure has not been stated into reports). This failure led to major downtime and spike in customer complaints. The company lost an estimated $66,240 per minutes during the failure, clearly showing the impact of performance failures.

Another similar incident comes from OTT giant Netflix, that led to an estimated loss of $1 million in potential revenues. Both incidents highlight the importance of investing into robust performance measures.

Performance testing is necessary to understand how the application would behave under real-world scenarios and under actual load and stress conditions. A rigorous performance testing helps in ensuring that application can handle stress and traffic spikes leading to improved user satisfaction, revenue protection, and any cascading effect of performance failures.

Objectives of Performance Testing

As the name says it all, Performance Testing is all about delivering top-notch application performance. The objectives of performance testing are to understand application’s behavior under various load and stress conditions and deliver seamless customer experience even during peak times. It helps in eliminating any performance bottlenecks and issues that impact application performance.

Other objectives of performance testing include testing scalability, improving reliability and stability of the system, resource utilization, identifying potential security vulnerabilities, support capacity planning and reduce overall risks of downtime and revenue losses.

Types of Performance Testing

Performance testing helps in various ways to test the running behavior of digital applications. In addition to test the application against production like load and volume, it helps in identifying the scalability of the application, identify security vulnerabilities and more. Thus, performance testing are categorized under various types –

1. Load Testing – Load testing assesses application performance against various workloads. While developing the applications, the workloads are nominal but as soon as the applications go live, the workload can increase and that means concurrent users and transactions. Load testing is done to be assured that as workload increases, applications can function effectively. If the application fails to take up a certain workload, engineering amp up their development and performance tune the application.

2. Stress Testing – Stress testing is a form of load testing where applications are put under more than the expected loads. The intent of stress testing is to test the application to an extent where it breaks and identify the bottlenecks. This helps in improving the performance of application and improves it multiplicatively.

   A lot of platform companies have adopted a stress technique called Chaos Engineering, In Chaos Engineering, platforms are tested against various variable that can create performance or load issues in the platform. Unlike stress testing, chaos engineering tests platform as a whole and looks for multiple issues that can cause performance or stress issues.

3. Scalability Testing – Scalability testing is done to understand how effectively your system can adjusts when the workload suddenly increases or decreases. When carried out on application’s infrastructure and databases, it shows the capability of virtual or physical servers to adapt to traffic spikes or decrease in traffic. With virtualization getting popularity, a lot of engineering teams are using virtual servers to scale up and down their requirements.

4. Spike Testing –Spike testing tests application against sudden increase or decrease in workloads, while this sounds like a load testing, the objective of spike testing is to test the recovery times between the average spikes and does it require manual intervention.

5. Endurance Testing –Endurance testing is a form of spike or stress testing to check whether application can sustain the workloads for prolonged periods. Endurance testing helps engineering in preparing applications for any performance degradation, memory leaks, and security vulnerabilities. These issues are not visible when applications are exposed to increased workloads for shorter periods but might be visible when exposed to high load conditions for longer periods.

Challenges in Performance Testing

Before jumping into the best practices and leading tools for Performance Testing, it is necessary to understand challenges that come across in performance testing journey. This helps in identifying the elements that go into the making of effective performance testing strategy.

1. Complexity Challenges – Performance testing involves lots of systems, data, applications, resources (including software and hardware), and environment complexities. The multiple layers of technology, systems, and data makes it easy to derive a fit-in-the box performance testing strategy. Similarly, performance testing can involve multiple environments and longer periods of duration to test applications from various angles like stress, load, spike, and endurance testing.

   Solution – Invest in right pool of talent, if software development and testing is not your industry, outsource your performance engineering to team of experts that can help you in solving the complexity challenges.

2. CI-CD Integration Challenges – CI-CD is meant to deliver software faster but integrating performance tests slow down the pipeline. Performance tests tend to run longer to tests application performance holistically and requires multiple environments and data to generate meaningful results. Further, measuring and monitoring performance metrics can be challenging within the CI-CD pipeline making the whole system very complex.

   Solution – Don’t rush in integrating performance suite in CI-CD pipelines. Also, all successful CI-CD teams know that integrating testing and performance in pipelines will slow down your delivery initially but guarantees high-end quality applications.

3. Scalability Issues – Achieving scalability in performance testing can be a mammoth task. The combination of hardware, software, data, screens, and devices, and putting them together to achieve the highest level of application performance can really take a toll on software development teams. Moreover, the infrastructure that supports the applications can have limitations in terms of number of servers, database size, and network bandwidth.

   Solution – Using the right performance test automation tools and designing a scalable performance test automation suite will help teams in resolving scalability challenges.

4. Performance versus Functionalities –Finding the right balance between application functionalities and performance testing is a tough game. A lot of times developers need to develop functionalities to achieve the complete essence of the application but these functionalities compromise performance. Finding a middle ground is where Performance Engineering plays a vital role.

   Solution – Understand your product owner’s point of view and decide what functionalities cannot be compromised against performance. While it is tough to balance both, the team of performance testing experts can help you in choosing right functionalities and performance.

5. Lack of Visibility –With multiple tech stack and tool chains, it becomes really challenging for testers to pinpoint the exact cause of performance issue. Additionally, absence of centralized reporting for performance testing makes it difficult to remediation of issues making proactive fixes virtually impossible.

   Solution – Investing in an advanced test reporting and performance test automation tools will give required visibility across the performance testing lifecycle.

Performance Testing Workflow

1. Identify Performance Objectives and Metrics – The very first step in developing an effective performance testing workflow is to understand the application features and business scalability. This helps in determining the performance objectives and metrics that coincides with the business objectives and performance.

   While determining performance objectives, it is important to involve various stakeholders to understand various performance metrics and how they want application to behave. At Enhops, we conduct extensive discovery sessions with our clients and their teams to understand their application vision and recommend best performance testing strategies.

2. Performance Testing Requirements Gathering – Requirements gathering refersto the stage in which all requirements related to testing are gathered and documented. It’s an extensive document prepared to define the scope, performance goals, test environment and performance metrics.

   In this phase, it is essential to prepare an extensive document so that the application’s performance goals are clearly defined and testing efforts are tuned to meet those goals. It is also important to involve different stakeholders at this stage to frame a robust and all-inclusive performance testing strategy.

3. Establish Performance Acceptance Criteria – Performance Acceptance Criteria helps in pre-establishing performance goals and meeting the desired performance standards.Some of the examples of determining performance criteria are identifying the response time, throughput, resource-use goals, load time, memory usage per user, resolution time and more.

   Entry Criteria: Approved Test Requirements Document, Approved Test Plan and Test Specifications

   Exit Criteria: Approved Performance Metrics

4. Performance Test Planning – The performance test planning involves outlining performance test goals, application testing scope, performance metrics, and entry and exit criteria with a specific pre-determined schedule.

   Entry Criteria: Approved Performance Test Plan

   Exit Criteria: Approved Test Plan, Completed Test Strategy, and Test Schedule

5. Identify tools and set up testing environment – This stage involves identifying the preparing the test environment, prepare tools, and get team used to the testing environment. This involves documenting hardware, software, and infrastructure specifications to avoid confusions later. Test environments are also being testing to ensure complete replicability with the production environment to test applications in near real-time production.

   Entry Criteria: Approved Hardware and Software Requirements

   Exit Criteria: Environment Set-Up Documents, Functioning Test Environment

6. Plan and Design Performance Tests – After defining the established performance criteria, identify the performance test scenarios to develop test cases. These scenarios will outline the application behavior and metrics in normal-usage, over-usage, and under-usage scenarios. Based on the identified scenarios, teams start to put test data and scripts together for execution.

   Entry Criteria: Approved Performance Test Plan and Test Strategy

   Exit Criteria: Completed Test Design Document, Approved Test Cases, and Test Data

7. Performance Test Execution – Performance test execution involves running the performance tests and collects various performance metrics like response time, throughput, and resource utilization. Performance tests are executed based on various scenarios and workload profiles. The test data used in execution helps in understanding the application behavior under various workloads.

   Entry Criteria: Completed Test Design Document, Approved Test Cases, and Test Data

   Exit Criteria: Completed Test Execution, Test Logs, Test Results, and Performance Metrics

8. Continuously Monitor and Improve – Once the tests are executed, the metrics are collected and reviewed against the desired benchmarks. The teams then work on continuously monitoring and improving the test results to make small improvements in the application performance.

   Entry Criteria: Approved benchmarks for performance testing, Current performance testing metrics

   Exit Criteria:  There’s no exit in continuously improving performance metrics. Engineers continuously monitors and remediates issues to reach the best performance metrics and suggests innovation pockets where metrices can be improved further.

Performance Metrics

Tracking performance metrics is crucial because it gives a direct view about how increasing performance testing automation can help in improving high-value metrics and directly contribute to increased user satisfaction. Here are some of the performance metrics that must be tracked and constantly improved –

1. Response Time – Response time is an important performance testing metrics that tracks how much time does an application takes to respond to a user query. A lower response time can lead to frustrated users and thus, lowering the customer happiness quotient.

   There are several ways of continuously improving response time that includes optimizing system configuration and fine-tuning parameters such as CPU, disk usage, network bandwidth, implementing intelligent virtual machines, and optimizing database queries. For B2C applications, applying caching mechanisms also help in improving response time and reduces overall load on the applications.

2. Throughput –Throughput is second important performance metric that all engineering teams strive to improve continuously. Usually for high traffic and data-intensive applications, throughput is measured as the ability to handle concurrent requests at the same time. This means how efficiently and effectively an application can handle many requests or transactions.

   For eCommerce, healthcare, and direct-to-consumer applications, it is very important to have a high throughput performance. Several ways of improving application throughput are optimizing load balance so that applications don’t crash even in the high load conditions. Another way of improving throughput is optimizing application code and remove unnecessary operations like loops, variables, and conditions that increase processing time and reduce performance.

3. Resource Utilization – Resource utilization measures the optimal usage of physical and virtual hardware like CPU, memory, network bandwidth, and virtual hardware. This helps in optimizing the application performance by ensuring the application doesn’t over-use or under-use the memory and required hardware.

   Some best practices that help in streamlining and improving resource utilization are using load balancing and monitoring tools to distribute workloads across servers. Another way is to optimize application code to reduce memory usage and implement memory profiling tools to determine any memory leakage.

4. Network Latency – Network latency usually determines time taken for data to travel between client network and server network. The high network latency can result into poor user experience and higher customer churn rate.

   Several ways of reducing network latency are using content delivery networks, using network optimizing tools that helps in pinpointing any latency issues, moving physical servers closer to location, and moving to virtual servers.

5. Error Rate– Error rate helps in assessing how efficiently an application is performing by measuring failed transactions in applications. In B2B applications, error rates can help in ascertaining delayed timelines and supply chains that can affect important business decisions leading to loss in revenues. For B2C applications, high error rates can directly impact profits and revenues and bad customer word of mouth.

   Several practices that help in improving error rates are invest in test automation practices, build fault-tolerant architecture, monitor application and error rates in real-time and prepare team for real-time fault resolutions.

Best Practices for Performance Testing

The evolving quality engineering landscape has compelled application development and quality engineering leaders from across industries to invest in better performance testing measures. Given the rise in data, screens, networks, clouds, and devices, application performance has become a new business concern.

Engineering teams have no option but to bolster their application performance strategies by implementing several best practices explained below –

• Define clear performance goals – Having a blueprint of clear performance goals helps team in preparing the performance strategies from Day one. These goals server as North Star for teams and helps in achieving the business objectives.
• Invest time and efforts in creating realistic test scenarios – Performance testing is like hardening your application to thrive in real-time production data. Hence, it is advisable to create scenarios that replicate real-time data.
• Test Early, Test Often – Due to unrealistic pressure deadlines, it is important to shift performance testing to the left and start talking about performance even before the actual development and testing starts. This means building performance-oriented architecture and prepare applications for business scalability.
• Use realistic data – Data can be tricky when it comes to performance testing. Always use good and accurate near, real-time data for performance testing. This prepares applications to handle large dataset volumes and if they fail, engineers can look for remedies in the early stages leaving zero to no room for performance failures.
• Use Performance Test Automation tools: Don’t leave in a world of denial, manual testing is outdated and if you are still relying on it, you are already few miles away from your competitors. Work with specialized testing partners that can help you in selecting the right performance test automation tools and implementing them seamlessly across your CI-CD pipelines.
• Hire Performance Engineering Experts: Understanding and implementing performance goals can be a mammoth task, hire a team of performance engineers based on your application technology and invest in their training and continuous learning programs.
• Take Performance Innovation seriously: Usually, performance engineering becomes all about right CPU, memory, and network utilization. Look beyond the usual ways and best practices and let your team experiment with new concepts like site reliability engineering, chaos engineering, to understand what can help them in improving the performance.

How Enhops can Help?

Enhops is a leading software testing and quality assurance firm that helps companies in accelerating their digital transformation and quality engineering transformation plans. Our capabilities include performance testing, test automation, agile testing, DevOps and CI-CD, and more.

At Enhops, we have helped multiple companies improve their performance testing plans by implementing industry best practices and frameworks. We have partnered with multiple leading tools across the globe that share same mission of transforming software quality for businesses across the globe.

Do you have a performance testing project in mind? Send us an email marketing@enhops.com