Performance testing: best practices, metrics & more

Explore the essentials of performance testing, including types and automation with NeoLoad. Learn why automated testing is crucial for agility and DevOps.

What is performance testing?

Performance testing is the practice of evaluating how a system performs in terms of responsiveness and stability under a particular workload. Performance tests are typically executed to examine speed, robustness, reliability, and application size.

The process incorporates performance indicators such as:

• Browser, page, and network response times
• Server request processing times
• Acceptable concurrent user volumes
• Processor memory consumption; number and type of errors that might be encountered with app

Why is system performance testing important?

Performance testing will help you ensure that your software meets the expected levels of service and provides a positive user experience.

Applications released to the public in the absence of testing could suffer from a damaged brand reputation, in some cases, irrevocably so. Test results will highlight improvements you should make relative to speed, stability, and scalability before your app goes into production.

The adoption, success, and productivity of applications depend directly on the proper implementation of performance testing.

While resolving production performance problems can be extremely expensive, the use of a continuous optimization performance testing strategy is key to the success of an effective overarching digital strategy.

The use of a continuous optimization performance testing strategy is key to the success of an effective overarching digital strategy.

When is the right time to conduct performance testing?

Whether it’s for web or mobile applications, the life cycle of an application includes two phases: development and deployment. In each case, operational teams expose the application to end users of the product architecture during testing. Development performance tests focus on components (web services, microservices, APIs).

The earlier the components of an application are tested, the sooner an anomaly can be detected and, usually, the lower the cost of rectification. As the application starts to take shape, performance tests should become more and more extensive.

In some cases, they may be carried out during deployment (for example, when it’s difficult or expensive to replicate a production environment in the development lab).

Performance testing types

Different types of performance testing are conducted throughout the development life cycle to ensure that the application meets performance requirements and user expectations. Here are the primary types of performance testing:

1. Load tests

Load tests simulate the number of virtual users who might use an application. In reproducing realistic usage and load conditions based on response times, this test can help identify potential bottlenecks.

It also enables you to understand whether it’s necessary to adjust the size of an application’s architecture.

2. Unit tests

Unit tests simulate the transactional activity of a functional test campaign; their goal is to isolate transactions that could disrupt the system.

3. Stress tests

Stress tests evaluate the behavior of systems during peak activity. These tests significantly and continuously increase the number of users during the testing period.

4. Soak tests

Soak tests increase the number of concurrent users and monitor the behavior of the system over a more extended period.

The objective is to observe whether intense and sustained activity over time shows a potential drop in performance levels, making excessive demands on the resources of the system.

5. Spike tests

Spike tests seek to uncover implications to the system operations when activity levels are above average. Unlike stress testing, spike testing considers the number of users and the complexity of actions performed (hence the increase in several business processes generated).

6. Volume tests

Volume tests focus on assessing the system’s ability to handle a large volume of data.

They evaluate how the application performs when subjected to a significant amount of data input, such as large databases or files, ensuring that performance does not degrade as data volume increases.

7. Endurance tests

Endurance Tests assess the system’s stability and performance over an extended period under a consistent workload.

They aim to uncover any memory leaks, performance degradation, or other issues that may occur when the application runs continuously for hours, days, or even weeks.

8. Compatibility tests

Compatibility tests assess the application’s performance across different environments, platforms, devices, and configurations.

They ensure that the application performs optimally on various operating systems, browsers, network conditions, and hardware setups, providing a consistent user experience across different environments.

Regression tests assess whether recent changes to the application code have impacted its performance negatively.

9. Regression tests

Regression tests assess whether recent changes to the application code have impacted its performance negatively.

They help ensure that new features, updates, or fixes do not introduce performance regressions or degrade the overall system performance compared to previous versions.

10. Scalability tests

Scalability tests evaluate how well the application can scale up or down to accommodate changes in workload or user demand.

They assess the system’s ability to maintain performance levels as the number of users, transactions, or data volume increases or decreases, helping identify scalability limitations and bottlenecks.

11. Endurance tests

Endurance tests assess the system’s stability and performance over an extended period under a consistent workload.

They aim to uncover any memory leaks, performance degradation, or other issues that may occur when the application runs continuously for hours, days, or even weeks, ensuring long-term reliability and robustness.

12. Resilience tests

Resilience tests evaluate the application’s ability to withstand and recover from failures or disruptions gracefully.

They simulate various failure scenarios, such as network outages, server crashes, or database failures, to assess how the application responds and recovers without data loss or significant downtime.

13. Reliability tests

Reliability tests assess the system’s stability, availability, and resilience under real-world conditions, simulating failure scenarios and adverse conditions.

They validate the system’s ability to maintain consistent performance and functionality over time, ensuring reliable operation in production environments.

What is the difference between load testing and performance testing?

Load testing and performance testing are often used interchangeably. The terms are also sometimes used as though they’re at odds. Both stances are wrong.

Load testing is one of many types of performance testing; the most important ones also include unit, stress, soak, and spike tests.

Load testing checks the system’s performance under a desired level of user traffic. You can simulate a realistic number of simultaneous users and monitor whether the system maintains acceptable response times and low error rates.

The aim is to ensure your system can manage the expected demand without performance decline. Load testing ensures that your system can handle expected load, and performance testing examines how it reacts under all conditions, including edge cases.

What is the difference between performance testing and performance engineering?

Performance testing assesses a system’s behavior under a defined workload. It does this by simulating user traffic and measuring the key performance indicators. The aim is to collect information on the system’s capacity and stability under load.

Performance engineering extends beyond testing to include design and optimization. It also entails architectural choices. For example, selecting efficient data structures, caching strategies, database queries, and concurrency handling options.

Performance engineering seeks to avoid problems in engineering systems that are intrinsically scalable and efficient, rather than responding to them.

Testing helps find problems, while engineering aims to prevent performance issues before they happen. Glenford J. Myers notes, “Testing is the process of executing a program with the intent of finding errors.”

Performance testing can be used to analyze success metrics like response times and potential errors.

What does performance testing measure?

Performance testing can be used to analyze success metrics like response times and potential errors. With these performance results in hand, you can confidently identify bottlenecks, bugs, and mistakes—and decide how to optimize your application to eliminate the problem(s).

The most common issues highlighted by performance tests are related to speed, response times, load times, and scalability.

1. Load time

Excessive load time is longer than the time required to start an application. Any delay should be as short as possible—a few seconds, at most, to offer the best possible user experience.

2. Response time

Poor response time elapses between a user entering information into an app and the app’s response to that action. Long response times significantly impact the user experience.

3. Scalability

Limited scalability occurs when an app has poor adaptability to differing numbers of users, such as when the app performs well with just a few concurrent users but deteriorates as user numbers increase.

4. Bottlenecks

Bottlenecks are obstructions in the system that decrease the overall performance of an application. They are usually caused by hardware problems or poorly written code.

Automated performance testing: Why it’s essential for modern Devops

Automated performance testing integrates performance validation directly into the software development life cycle.

Instead of running manual performance tests late in the process—often under pressure—automation allows teams to consistently test for speed, scalability, and reliability at every stage.

This shift enables earlier detection of performance issues, reduces costs, and supports faster release cycles.

In Agile and DevOps environments, where rapid iteration is standard, automation is the only viable way to keep performance testing in step with development.

Automated tests can be triggered as part of continuous integration (CI) pipelines, ensuring every code change is evaluated for its impact on application performance. This means regressions, bottlenecks, or performance degradation are caught before they reach production.

Automation also enhances test coverage. It allows testers to simulate complex user behaviors and traffic patterns across multiple scenarios and environments without exhausting resources.

With tools like NeoLoad and other performance testing platforms, teams can design, execute, and analyze performance tests with minimal manual effort. Many of these tools offer codeless options, making automation accessible even to teams with limited scripting expertise.

However, successful automated performance testing isn’t just about running scripts. It requires proper test design, realistic workloads, well-defined success criteria, and integrated monitoring.

Automated test results must be actionable, enabling teams to quickly diagnose root causes and optimize application behavior under load.

Ultimately, automation transforms performance testing from a bottleneck into a continuous feedback loop.

It reduces the risk of performance failures in production, improves user experience, and supports the high velocity demanded by today’s digital products. As systems grow more complex, automation isn’t just helpful—it’s essential.

What is the performance testing process?

While testing methodology can vary, there is a generic framework you can use to identify weaknesses and ensure that everything will work properly in various circumstances.

Before you begin the testing process, it’s essential to understand the details of the hardware, software, and network configurations you’ll be using.

1. Identify the testing environment

Before you begin the testing process, it’s essential to understand the details of the hardware, software, and network configurations you’ll be using. Comprehensive knowledge of this environment makes it easier to identify problems that testers may encounter.

2. Identify performance acceptance criteria

Before conducting the tests, you must clearly define the success criteria for the application, as it will not always be the same for each project. When you are unable to determine your success criteria, it’s recommended that you use a similar application as the benchmark.

3. Define planning and performance testing scenarios

To carry out reliable tests, it’s necessary to determine how different types of users might use your application. Identifying key scenarios and data points is essential for conducting tests as close to real conditions as possible.

4. Set up the testing environment

Begin by configuring the testing environment to mirror the production setup. This includes setting up servers, databases, and network configurations to closely replicate real-world conditions.

Ensure that the application under test (AUT) is deployed in this environment. Integrate monitoring tools to collect performance metrics during testing.

5. Implement test design

Develop test scripts and scenarios based on predefined objectives and acceptance criteria. These scripts should emulate various user interactions and system behaviors.

Ensure that the test design aligns with identified key scenarios and data points for realistic testing. Cover different types of tests such as load testing, stress testing, and scalability testing.

6. Run and monitor tests

Execute the prepared test scripts in the configured environment. Monitor system performance metrics in real-time to evaluate response times, throughput, and resource utilization.

Keep a close eye on the test environment for any anomalies or performance bottlenecks. Continuously observe test progress and note any deviations from expected behavior.

7. Analyze, adjust and re-do the tests

Analyze and consolidate your test results. Once the necessary changes are completed, tests should be repeated to ensure the elimination of any other errors.

Widely used performance testing tools

Here are some commonly used performance testing tools:

Tricentis NeoLoad

Tricentis NeoLoad is a low-code performance testing platform for enterprise applications. It eases complex testing in large organizations with CI/CD integration.

k6

K6 is a modern, developer-friendly performance testing tool. It writes JavaScript test scripts and executes them either with CLI or CI/CD pipelines. Most suitable for API and microservice testing. Lightweight and efficient.

LoadRunner

Experienced enterprise tool with the largest legacy protocol. When considering mission-critical applications that require certification, OpenText LoadRunner is the best choice.

Locust

Locust is Python-based. It specifies user behavior as code. You can change the load in real time via a web UI. Performance: Ideal with Python-intensive groups.

BlazeMeter

BlazeMeter is a Cloud platform that executes JMeter, k6, and Gatling scripts at scale. It allows cloud-native, easily scalable testing without dealing with infrastructure.

Apache JMeter

JMeter is one of the most popular open-source performance testing tools. Works with thread groups to model user traffic between APIs, databases, and web apps. It’s mature, protocol-flexible, and widely used in enterprise settings.

Gatling

Gatling is Scala-based asynchronous high-performance. Generates exquisite, richly detailed HTML reports. Optimal with JVM teams that require in-depth analytics.

Artillery

Artillery is an API/microservice load testing toolkit for the modern world. Relies on YAML or JavaScript to specify test scenarios and run distributed loads. It’s lightweight and fits well in serverless and cloud-native systems.

Does performance testing require coding?

A question that troubles many aspiring performance testers is whether performance testing requires coding. Performance testing doesn’t require coding because there are performance testing tools that choose a codeless approach.

On the other hand, some tools rely on coding. Whether you choose a codeless or code-based approach depends on many factors, but primarily the coding experience and knowledge of your team members.

Performance testing does not require coding because there are performance testing tools that choose a codeless approach.

What are the characteristics of effective performance testing?

Realistic tests that provide sufficient analysis depth are vital ingredients of “good” performance tests. It’s not only about simulating large numbers of transactions, but also anticipating real user scenarios that provide insight into how your product will perform live.

Performance tests generate vast amounts of data.

The best performance tests are those that enable quick, accurate analysis to identify all performance problems and their causes.

With the emergence of Agile development methodologies and DevOps practices, performance tests must remain reliable while keeping pace with the accelerated software development life cycle.

To keep pace, companies are looking to automation, with many choosing NeoLoad—the fastest and most highly automated performance testing tool for the design, filtering, and analysis of testing data.

Tips for performance testing

Some tips to consider when doing performance testing:

1. Create a repeatable baseline

Conduct a low-load test before making any changes. This way, you get an idea of normal response times and resource consumption. With a baseline, you can tell whether there is real degradation or normal variation.

2. Check that your load generator is not the bottleneck

Trace CPU, memory, and network ports on your test machine. A saturated load generator generates false failure reports.

3. Test using data volume like production

A database with 100 rows is not at all like one with 10 million rows. Simulate your environment with realistic data sizes and distributions.

4. Isolate the environment from noise

Turn off cron jobs, backups, log rotation, and monitoring agents that may run during the test.

5. Avoid caching by rotating test data

Repeated searches with the same search terms or user IDs yield artificially high cache hit rates. Script your parameterization to exercise cold paths.

6. Measure system restore time

After halting traffic, measure how long it takes the system to resume normal resource usage. Leaks show themselves through slow recovery.

7. Add realistic thinking time between actions

Actual users often take a break of 2-10 seconds between clicks. When thinking time is eliminated, a 100-user DDoS attack becomes an unrealistic 1000-user attack.

8. Track the 99th percentile rather than the average

The fact that 1 out of 100 users has to wait 10 seconds will be hidden by an average response time of 200ms. User frustration resides in the tail.

9. Repeat each test three times

Network jitter, garbage collection, and OS scheduling cause natural variance. An outcome that you cannot repeat three times is not robust.

Clearly define the critical metrics you will be looking for in your tests.

Performance testing success metrics

Clearly define the critical metrics you will be looking for in your tests. These metrics generally include:

1. Memory usage: Use of a computer’s physical memory for processing.
2. Network bandwidth: Number of bits per second used by the network interface.
3. Disk I/O busy time: Time the disk is busy with read/write requests.
4. Private memory: Number of bytes used by a process that cannot be shared with others.
5. Virtual memory: Amount of virtual memory used.
6. Page faults: Number of pages written or read to disk to resolve hardware page defects.
7. Page fault rate: Overall processing rate of faulty pages by the processor.
8. Hardware interrupts: Average number of hardware interruptions the processor receives/processes each second.
9. Disk I/O queue length: Average read/write requests queued for the selected disk during a sampling interval.
10. Packet queue length: Length of the output packet queue.
11. Network throughput: Total number of bytes sent/received by the interface per second.
12. Response time: Time taken to respond to a request.
13. Request rate: Rate at which a computer/network receives requests per second.
14. Pooled connection reuse: Number of user requests satisfied by pooled connections.
15. Max concurrent sessions: Maximum number of sessions that can be simultaneously active.
16. Cached SQL statements: Number of SQL statements handled by cached data instead of expensive I/O operations.
17. Web server file access: Number of access requests to a file on a web server every second.
18. Recoverable data: Amount of data that can be restored at any time.
19. Locking efficiency: Efficiency of table and database locking mechanisms.
20. Max wait time: Longest time spent waiting for a resource.
21. Active threads: Number of threads currently running/active.
22. Garbage collection: Rate at which unused memory is returned to the system.

The goal of each performance tester is to prevent bottlenecks from forming in the Agile development process.

AI impacts on performance testing

Here’s how AI affects performance testing:

1. Automated test generation based on production traffic. AI records actual user flows and directly translates them into executable load-test scripts, eliminating manual scripting.
2. Intelligent root cause analysis. AI compares measurements between logs, traces, and infrastructure to identify precise bottlenecks rather than symptoms.
3. Self-healing test scripts. AI automatically fixes broken scripts when application UI or API endpoints evolve, removing the overhead of manual maintenance.
4. Simulation of realistic loads based on production. AI uses real user behavior logs to generate stochastic traffic patterns rather than simple linear ramp-ups.
5. Stateless anomaly detection. AI learns the system’s normal behavior and alerts to anomalies well before reaching predetermined thresholds.
6. AI translates complex performance graphs into plain English statements. For example: “The login API failed the SLA for 500 users due to database connection pool exhaustion.”
7. Predictive capacity planning. AI predicts future bottlenecks by relying on historical trends and growth rates, and it will respond to the question of when we’ll need to scale before it goes wrong.
8. Constant production feedback mechanisms. The gap between test and real-world conditions is closed, as AI monitors live systems and automatically initiates performance tests when anomalies are detected.
9. Reduced false positives. AI differentiates between normal performance variance and actual problems, and removes threshold-based monitoring as a source of alert fatigue.

Who benefits from performance testing?

Here’s a list of people who benefit from performance testing:

1. End users

Performance testing ensures users can access the site during peak traffic. They interact with it without error. A well-performing application will create a sense of trust and satisfaction, safeguarding users’ time, patience, and loyalty.

2. Developers

Performance testing gives a clear picture of the issues at the code level before it goes to production. It also provides developers with objective measures to show that their optimization efforts have indeed paid off.

3. Quality assurance (QA) and testing teams

Performance testing helps software testers move from simply fixing bugs to actively ensuring users have a good experience. It provides a systematic, repeatable way to verify non-functional requirements. It also eliminates false bug reports because of environmental slowness.

4. Security and compliance teams

Performance testing helps security teams detect denial-of-service, rate-limiting, and resource-overload attacks before malicious attackers exploit them.

Performance testing is also used to meet compliance criteria, such as service availability in regulated industries like finance or healthcare.

5. Site reliability engineering (SRE) and operations

Performance testing will provide them with assurance in capacity planning, autoscaling guidelines, and disaster recovery processes.

Ops teams can rest easy during large-scale sales or product releases because they know the system has been tested with 2x the anticipated peak traffic.

Additionally, performance tests, including recovery scenarios, ensure that monitoring, alerting, and failover are in fact functioning as intended.

Through performance testing, leaders can determine the exact capacity required to meet demand.

6. Finance and leadership

A buggy e-commerce website or application directly causes reduced conversion rates, increased cart abandonment, and decreased customer lifetime value. Even an hour of downtime during prime shopping time can be very costly.

Performance testing is an insurance policy against such losses. Through performance testing, leaders can determine the exact capacity required to meet demand.

7. Product managers/ business owners

This is beneficial to product managers, as performance testing de-risks roadmaps and feature launches.

A product manager who makes a promise of a Black Friday sale or a Super Bowl ad must be assured that the infrastructure will not fall. Performance testing gives that assurance in quantifiable form.

For example, imagine simulating 10,000 simultaneous users and getting response times of less than 500ms. Business owners can make aggressive marketing promises without worrying about technical humiliation or downtime-related revenue loss.

Why automate performance testing? For more agility!

Digital transformation is driving businesses to accelerate the pace of designing new services, applications, and features in the hope of gaining/maintaining a competitive advantage.

Agile development methodologies can provide a solution. Despite the adoption of continuous integration by Agile and DevOps environments, performance testing is typically a manual process.

The goal of each performance tester is to prevent bottlenecks from forming in the Agile development process. To avoid this, incorporating as much automation into the performance testing process where possible can help.

To do so, it’s necessary to run tests automatically in the context of continuous integration and to automate design and maintenance tasks whenever possible.

The complete automation of performance testing is possible during component testing. However, human intervention of performance engineers is still required to perform sophisticated tests on assembled applications.

The future of performance testing lies in automating testing at all stages of the application life cycle.