Concurrency testing: A detailed overview

“It is far easier to design a class to be thread-safe than to retrofit it for thread safety later.”
— Brian Goetz, Java Concurrency in Practice

Concurrency issues often lurk beneath the surface, only revealing themselves under specific conditions. Concurrency testing is designed to uncover these hidden problems by simulating multiple users or processes interacting with your application simultaneously. Therefore, in this post, we’ll explore what concurrency testing entails, its significance, and how to implement it effectively. Let’s get right into it.

What is concurrency testing?

Concurrency testing is a software testing technique that evaluates how an application behaves when multiple users or processes perform operations at the same time. In other words, it’s about simulating simultaneous usage to ensure the system can handle the load and that no issues (like data corruption or crashes) occur when everything happens at once.

In other words, concurrency testing answers questions like: Can our backend handle 500 people uploading a file at the same second? What if 1,000 bank transfers hit the database concurrently? Will two customers trying to book the last airline seat end up overselling it? By performing concurrency tests, you can identify bugs such as race conditions, deadlocks, and synchronization issues that might not appear with just one user at a time. These are the kinds of problems that often only surface under real-world conditions when many actions happen in parallel.

Concurrency testing comes in a few flavors, each simulating different real-world load patterns and conditions.

Types of concurrency testing

Concurrency testing comes in a few flavors, each simulating different real-world load patterns and conditions. You may hear these referred to as different types of performance tests, but they all involve concurrent activity. Let’s dive into the details.

Load Testing: Load testing assesses performance under expected normal and peak load conditions. Here, we gradually increase the number of concurrent users or transactions to the typical peak level to verify the system can handle it. For example, if you expect up to 5,000 users on your e-commerce site during peak hour, a load test will simulate those 5,000 concurrent shoppers to see if everything stays responsive.

Stress Testing: A stress test goes beyond normal load to find the breaking point. It keeps increasing concurrency (more users, more transactions) until the system either fails or performance degrades beyond acceptable levels. For instance, you might push that e-commerce system to 50,000 simultaneous users to see what breaks first and ensure that when pushed to extremes, it fails gracefully rather than corrupting data.

Spike Testing: Spike testing is about sudden bursts of concurrent load. Instead of a gradual ramp-up, a spike test slams the system with a rapid increase in users or requests to see how it copes with an abrupt surge. This mimics scenarios like everyone hitting a ticket website the minute concert tickets go on sale. The system must handle the immediate jump from, say, 100 to 5,000 users in a few seconds and then perhaps drop back down just as quickly.

Soak Testing: Soak tests check sustained concurrent load over a long period. Here, you might run a moderate-to-high load of concurrent users for many hours or days to observe system behavior over time. The goal is to find issues like memory leaks, gradual performance degradation, or database resource exhaustion that only emerge during prolonged use. For example, you may run 1,000 virtual users for eight hours overnight. If response times slowly creep up or the server crashes after six hours, that signals a problem like resource leakage or fatigue.

All these test types are complementary. The common thread is that they all subject the system to multiple operations at the same time. Not every project will require every type of test, for instance, a social media app expecting unpredictable viral traffic might prioritize spike and stress testing.

How concurrency testing works: key concepts and process

So, how do we perform concurrency testing? Concurrency testing simulates multiple users or processes interacting with your application at the same time. The goal is to evaluate how well the system handles overlapping operations and to surface issues like race conditions, data conflicts, or degraded performance. It all starts by identifying high-risk scenarios—think concurrent log-ins, simultaneous transactions, or batch updates—where user overlap is likely. From there, test scripts are created to replicate real user actions, often using tools like Tricentis NeoLoad or open-source options such as JMeter. These tools automate the execution of hundreds or thousands of virtual users performing the same or varied tasks in parallel.

The key to this process is configuring how concurrency unfolds: ramping up users slowly, spiking them suddenly, or sustaining load for endurance testing. Throughout execution, tools capture critical metrics like response times, throughput, error rates, and system resource usages that reveal bottlenecks or failures. For example, performance might degrade beyond 500 users, or a database deadlock may appear when two users modify the same record. However, intermittent bugs, subtle timing issues, or cascading failures require careful investigation to trace root causes. After fixes, re-running tests confirms improvements. In short, concurrency testing combines realistic simulation with deep monitoring to stress and validate systems under load.

The key to this process is configuring how concurrency unfolds: ramping up users slowly, spiking them suddenly, or sustaining load for endurance testing.

Benefits of concurrency testing

And why would you like to do concurrency testing? Well, because it ensures your application remains stable, fast, and reliable when multiple users or processes interact at the same time. One major benefit is catching hidden bugs like race conditions or data collisions that only appear under concurrent activity. These issues often don’t appear during standard single-user testing but can crash systems or corrupt data in production. Moreover, it also helps validate performance under pressure. When you simulate realistic traffic, you can confirm that response times and system throughput stay within acceptable limits, even as load increases. This is critical for user satisfaction and uptime during peak periods.

You also gain insights into scalability and capacity, like how many users your system can handle before things degrade, and this helps plan infrastructure and avoid surprises. Most importantly, concurrency testing protects the user experience. It verifies that critical workflows still function under load, reducing the risk of downtime or slowdowns during high-stakes moments like product launches or traffic spikes. In short, it’s a vital safety net that helps teams ship robust, production-ready software with confidence.

Challenges of concurrency testing

While concurrency testing is powerful, it’s also one of the most complex testing types to get right. First, it introduces technical complexity. Designing scenarios that accurately simulate real-world concurrency involves timing, synchronization, and thoughtful data handling. Bugs may only appear under specific, hard-to-reproduce conditions, making them tricky to debug. There’s also the need for a realistic, high-capacity test environment. Simulating hundreds or thousands of users requires significant infrastructure resources, which aren’t always easy to spin up or maintain.

Additionally, intermittent failures are another pain point. Concurrency issues can be non-deterministic, making them hard to catch or verify. You might see random errors that only happen under specific timing overlaps. Another challenge is that test results often require deep analysis. A spike in failures could be a real bug or a misconfigured environment, and when you differentiate between them, you need to have both system knowledge and investigative skill. Lastly, tool limitations or scripting errors can skew test results, especially when simulating complex behaviors. But despite these hurdles, the value of concurrency testing far outweighs the effort.

While concurrency testing is powerful, it’s also one of the most complex testing types to get right.

Concurrency testing and automation

Because of the difficulties mentioned above, automation is absolutely essential for concurrency testing. It’s one area of testing you simply cannot do effectively by hand beyond a very small scale. Let’s talk about what parts of concurrency testing can be automated and how tools can help.

1. Automating the Load Generation: Tools will create virtual users, handle timing, and generate concurrent requests far more precisely than any human-orchestrated effort. For example, Tricentis NeoLoad is designed to automate performance and concurrency tests by letting you build user scenarios and then execute those scenarios with hundreds or thousands of users in parallel. You hit “run”, and the tool takes care of spinning up threads, ramping users, repeating actions, etc. Similarly, open-source tools like JMeter or Locust use scripts or code to launch many threads.

2. Automating Data and Environment Setup: A big part of concurrency testing is setting up the right data and resetting the system to a known state for repeatability. Automation can help here by seeding databases with test data, or using scripts to create user accounts, etc., before the test runs. For instance, you might use scripts to deploy the latest build of your application on a staging environment, populate it with baseline data, run the concurrency tests, then tear it down, all in an automated pipeline.

3. Continuous Testing and Integration: This means after each deployment, your automated system spins up an environment, runs a suite of concurrency tests, and reports results automatically. If performance regresses or new concurrency issues appear, the pipeline can flag it. Tools like NeoLoad have plugins for Jenkins and other CI tools to trigger tests and collect results. This kind of automation ensures that concurrency testing isn’t a one-off activity but a continuous practice.

To sum up, automation is the engine that makes concurrency testing feasible. It handles the tedious and complex parts of orchestrating many simultaneous actions. With the right tools and good practices, you can embed concurrency testing into your regular test cycle.

With the right tools and good practices, you can embed concurrency testing into your regular test cycle.

Conclusion

Concurrency testing isn’t just a nice-to-have. In today’s world with a high-traffic environment, users rarely interact with your application one at a time. Whether it’s hundreds of people checking out during a flash sale or multiple services hitting your backend, concurrent activity is the norm, not the exception. We’ve explored what concurrency testing is, how it fits into the software development life cycle, and the types of tests that simulate different load patterns. We also walked through how it works: defining realistic scenarios, using automation tools like Tricentis NeoLoad to simulate load, and analyzing results to catch performance issues and concurrency bugs before users do.

Despite challenges like setup complexity and non-deterministic bugs, the benefits are substantial: better reliability, improved performance, and confidence that your system won’t fail under pressure. Automating these tests and integrating them into your CI/CD pipeline ensures they’re not just a one-time effort but part of your ongoing quality strategy.

If you’re serious about scalability and resilience, concurrency testing should be a core part of your QA toolkit.

David Snatch wrote this post. David is a cloud architect focused on implementing secure continuous delivery pipelines using Terraform, Kubernetes, and any other awesome tech that helps customers deliver results.