How Performance Testing is Changing in 2022

By performance testing applications, engineering teams can deliver code in confidence. Therefore, developers and QA invest a lot of their resources and time in testing their code. But testing is not a siloed activity. Instead, it changes and adapts to engineering needs.

With the transition from waterfall development, to agile deliveries, testing shifted left from the Center of Excellence. When the need to improve efficiency and increase coverage grew, test automation was introduced. And when there was a growing need to democratize testing, open source testing projects flourished.

Today, the changes in modern development and the digital transformation enterprises are going through, from legacy systems to cloud-native microservices, require testing practices to adapt as well. Additional trends and global changes like privacy regulations, cybersecurity attacks, the digitization of customer and supply-chain interactions and increased leadership attention to digital infrastructure - are also impacting testing methods. Finally, the pandemic has completely shaken up the way we work and interact, and testing has not been excluded from this tendency.

How will all of these major shifts affect testing in 2022? We asked our customers what the biggest challenges they expected to face in 2022 were. Here’s what they answered -

8 Continuous Testing Challenges in 2022

Challenge #1 - Generating Test Data

Test data is required to ensure tests are reliable and accurate. If the data is detailed and of high quality, the tests will also be. For testing, data needs to be comprehensive, cover a wide variety of use cases and fit testing environment requirements.

However, obtaining test data is not an easy feat. It requires gathering data from production and adapting it to the testing requirements. Even when you have all the correct types of data, it still needs to be obfuscated, randomized and replicated for production, in itself a resource-intensive task. This is important for ensuring privacy and security. 

In 2022, as the number and need for tests is expected to grow, finding and scrubbing real data is expected to pose a significant obstacle for running performance tests.

The Solution: Synthetic Data

Synthetic data can answer growing data needs, as synthetic data is unlimited, unbiased and non-personal. Synthetic data can be generated on-demand, according to any requirement and test type. In addition, since it’s not real data, there’s no compliance or security risk.

BlazeData enables generating and managing test data. Read more here and here.

📕 Related Resource: Learn more about Security vs. Compliance: What’s The Difference?

Challenge #2 - Load Scaling Orchestration

Load testing determines the number of users that applications and systems can serve before negatively impacting performance and/or functionality. Today, many teams manually calibrate the number of users when running their load test, until they reach a CPU utilization or memory usage metric that is too high according to their internal KPIs. Needless to say, this process takes a lot of time and could be subject to multiple inaccuracies.

This is especially true when running types of load tests where it’s important to determine the exact point a system starts deteriorating. For example, when running a stress test before large events.

As more interactions become digitized in 2022, it’s important to be able to accurately determine systems’ limits, as well as make the most out of engineering talents’ time.

The Solution: Auto-Scaling

Automating user scaling while running tests is a more accurate, effective and efficient process. By letting machines test various numbers of users and providing the results, testers’ time is freed up for more quality activities like creating edge test scenarios. In addition, auto-scaling also enables scheduling these tests, so they run when we want them to, and not only when a tester is in the office.

Challenge #3 - Simulating Real-World Conditions

The goal of testing is to ensure all errors and bugs are caught before features are deployed to the user. However, testing often takes place in testing and staging environments. While these enable catching many of the issues a user will experience, they do not replicate the production environment to a tee.

Production environments have “noise”, like dependencies, changing loads, rapid code changes, randomized transactions, various types of data, etc. When developing in a distributed architecture, the volumes of “noise” are much higher. Since this “noise” cannot be accurately tested in pre-production environments, there are bound to be bugs and errors that aren’t caught before production.

In addition, engineering teams are spending a lot of time and resources attempting to replicate production environments. Perhaps there’s a better use of their time, and a better solution for simulating production conditions.

The Solution: Shifting Left AND Shifting Right Your Testing

While there’s no replacement to shifting left testing and running your tests as early as possible in the development lifecycle, these tests should also be complemented by running tests in higher environments, including production. The New York Times engineering team, for example, runs some of their tests in production.

How, when and where to run the tests should be determined by the organization’s specific needs, but some solutions include blue-green testing, canary testing, testing during certain maintenance windows (for example on Sunday night), feature flagging, and more. In addition, integrating with an APM will help catch and pinpoint unforeseen issues when testing in production. Finally, using synthetic data (see point #1 can also help simulate real-world conditions in a lower environment).

Challenge #4 - Connecting Metrics to Business

A performance test report will include different metrics: error rate, hits per second, response time, etc. However, who’s to say if these results are good or bad, and what they mean for your business? In 2022, as business agility and optimization become a “make or break” criteria for business success, it’s important to be able to understand results so they can be turned into actionable business insights.

The Solution: Baseline Requirements

Building a performance baseline will help measure and enforce service level objectives, to ensure that system performance and functionality are optimal. To build a baseline, it’s recommended to aggregate historical data, requirements from stakeholders, business needs, and industry best practices, and to determine various performance thresholds.

When running tests, compare them to the baseline. This will help identify any errors so teams can fix issues before they have a meaningful impact on users. Learn more here.

Challenge #5 - The Pandemic

During the pandemic, lifestyle and work style changed drastically. The transition to remote work created a higher load on systems, as more communications became digital and relied on systems and infrastructure. This generated more need for load tests, to ensure system performance. In addition, due to the change in work patterns, users are now connecting from different locations at different times than before. These scenarios also needed to be tested.

Since the pandemic is far from over and it’s repercussions will still be with us years ahead, the impact on systems and their required performance is still not completely known. What we do know, is that the way things were is not necessarily the way they will be.

The Solution: Plan for the Unknown

When behavior patterns are unknown, it’s recommended to continuously run spike and stress tests to ensure systems are always ready, and that teams know how to react during a system failure to minimize impact. Strategically, it’s recommended to set aside more resources for more types of testing on a more frequent basis and across low and high environments, and to prepare alternative scenarios for dealing with various kinds of systems failures.

Challenge #6: Security Testing

In the past few years, cybersecurity attacks have become more prevalent. From the engineering side of things, some of the vulnerabilities companies are facing are the result of OSS libraries or coding best practices. In 2022, as the number and sophistication of attacks is expected to continue to grow, teams need to find a way to reduce the attack surface.

The Solution: Shifting Left Security

Many new tools and practices are encouraging shifting left security and incorporating techniques like scanning or coding methods as early as development. In addition, it’s recommended to run penetration tests and code analyses through security tooling. These help minimize the risks and ensure the product, the company and the company’s users are secure.

Challenge #7: Finding the Right Testing Tools

Ever-changing software needs also require new types of testing tools and capabilities. Tools that were adequate a decade ago for legacy systems, may no longer be right for testing microservices, and new tools may not provide a solution for testing your mainframe.

Even once a new tool is found, there is still a need to get management buy-in. This will ensure widespread usage, resource allocation and that a process is put in place to act on test results.

The Solution: Test Your Testing Tools

To minimize overhead of managing tools and to help advocating for them across the organization, it’s recommended to use tools that:

• Cover a wide range of testing requirements
• Can integrate with your CI/CD pipeline, and other infrastructure and stack
• Incorporate automation
• Support SaaS

These conditions will help increase tools adoption and effectiveness and so they are incorporated in company culture.

When looking for tools, try out their free tier and take a look at open source tools as well. Gather results in reports and dashboards, so you can show proof of capabilities to leadership. Once you choose a tool, keep management updated on progress and success.

Challenge #8: Load Testing Legacy Systems

Older, legacy code could still be essential for your company’s products and systems. Yet, as modern development is transitioning to cloud-based microservices, it becomes harder to run continuous testing and end-to-end testing to ensure performance and functionality of both old and new code.

In addition, such legacy code might not have been subject to load testing in the past. But with the growing number of users and new best practices for increasing testing coverage, it’s essential to test it and thus avoid failures or poor user experience.

The Solution: Integrate Your Testing Tool with APM

Find the right testing tool that can validate both your legacy and your new code. Integrate this tool with an APM so you can monitor results and ensure the “integration” between old and new code is seamless to your customers and that both legacy and new systems operate as a cohesive unit. Unless you plan on deprecating your legacy code, don’t give up on testing it.

Looking Forward to Performance Testing in 2022

As modern development practices are quickly evolving, testing is required to keep up. By adapting performance testing practices, tools and methods, teams can continue to ensure code quality and agile deliveries. The list above breaks down seven of the top challenges that engineering teams, developers and QA will be dealing with in 2022, from privacy concerns to the pandemic to cybersecurity attacks.

BlazeMeter is well-equipped to address your performance testing challenges in 2022, as the tool for both modern and legacy development and testing teams.

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Related Resources

• Performance Testing vs. Load Testing vs. Stress Testing