How Performance Engineering Enables DevOps

“Performance is not just the responsibility of the developers, tester, or operations team. It is everyone’s responsibility, and ‘performance first’ should be the mantra of every stakeholder.” — Gartner

Availability, mean time to recovery (MTTR), and response time are among the many non-functional specifications that make up a software enterprise build’s performance. If the software doesn’t perform in real-world client situations, it fails — even if it meets all functional specifications.

The goal of applications development is to provide customer applications that meet functional and performance needs. Both elements are critical to success. Given the competitive landscape, customers won’t tolerate either functional or performance flaws. Transactions need to work as designed within the required timeframe. The overall success of online and mobile applications in fulfilling these standards has raised the bar on what customers view as acceptable performance.

Performance engineering is the process of ensuring that non-functional elements meet or exceed standards. The discipline goes beyond implementing continuous testing in all phases of the development lifecycle. Performance engineering has four major components:

  • Balancing performance priorities across developers, testers, and performance engineers
  • Providing for broad-based integrated performance engineering in the continuous integration and continuous deployment (CI/CD) process
  • Monitoring performance throughout the lifecycle (from build to production and post-production)
  • Using performance data to collaborate across teams to improve performance

Performance engineering is everyone’s responsibility, not just the performance engineers. It’s a continuous process designed into the entire DevOps process, not relegated to the end of the cycle.

Performance engineering is data-driven. It needs tooling to integrate it into DevOps processes to provide this data. Performance engineering tooling works best when viewed as part of the DevOps cycle rather than an add-on.

While continuous testing is an essential element of performance engineering, it’s only a part of the discipline. Although some performance engineering is automated, it still takes people with a variety of expertise to work together to implement it successfully. Where performance was previously relegated to the tail end of the lifecycle, performance engineering integrates it throughout the process.

Let’s explore how performance engineering enables DevOps.

Shift-Left Testing

DevOps is about developing and deploying applications to production as rapidly as possible. The DevOps process has emphasized ensuring the application meets the customer’s functional expectations. The non-functional elements have traditionally taken a back seat to functional needs in DevOps methodologies.

But how an application behaves in a production environment is critical to this task. A website will fail if it can’t meet its customers’ performance expectations. Performance engineering moves the exploration of non-functional aspects earlier into the development cycle through shift-left testing.

Since the application code is simpler in the early development stages, this testing occurs when the application code is more straightforward, making errors easier to detect and remediate. Also, because team members discuss and fix errors collaboratively, they can engineer solutions for future development and avoid rather than remediate defects. The result is reduced testing time and cost with improved software quality.

Integrating shift-left testing into the CI/CD pipeline enables unit testing and test script automation. Rather than permitting developers to code until near the production deadline, shift-left testing provides the information developers need to modify the code before it moves downstream. For example, the earlier you answer the question “Does the new release introduce defects that impact the customer?”, the quicker you can resolve issues.

You can integrate shift-left testing into your Agile DevOps cycle at any point. For example, when your team adds new functionality to the CI/CD pipeline, it’s beneficial to unit test performance before and after commitment to determine its effect. Similarly, if you’re expanding the types of production environments, it’s helpful to understand performance before and after the addition.

Shift-Right Testing

Shift-right testing is the process of continually testing applications or software after deployment in its actual production environment. It discovers defects that teams missed in the development environment.

Software is deployed to various environments, from multi-cloud to mobile, in all combinations and versions. These environments are in almost constant change because of technological advances, security patches, and other factors. Shift-right testing uncovers new issues before the customer does.

The goal of shift-right is increasing stability for the customer. It also is designed to future-proof the application. Having spent considerable resources rolling out the functionality, you don’t want some of your customers to lose the function because of changes to the production environment.

Shift-right also helps improve the twin metrics of MTTR and availability. If testing uncovers an issue, planning a solution or temporary workaround improves these metrics.

Implementing Performance Engineering

Shift-left and shift-right testing provide the data that feeds performance engineering. Performance engineering is about analyzing this data and working collaboratively across DevOps to build performance into software continuously throughout its lifecycle.

In the world of software, from development to deployment and customer use, the environment is constantly changing. The changes may come from deploying additional customer functions or changing the host environment’s security. Implementing a performance engineering program fed by continuous testing and analytics is a priority in coping with these changes and minimizing their impact on the customer.

While performance engineering is everyone’s concern, you still want to minimize its impact on their other job functions. Automation, reuse, and integration are three pillars that help.

One of testing’s more time-consuming elements is developing a test script. Scripts simulate how users will interact with the application that you are testing. Creating a testing script takes time away from development. You want to be able to minimize the script development time, as well as make scripts a reusable asset.

Having an online editor that records your navigation as you go through a business process is one way to help speed test development. Once it captures the basics, you can edit and adapt these actions to create various scripts representing users’ actions. You can replay them in volume to simulate performance in multiple environments and gather and analyze test performance data. This test development functionality needs to be available across your integrated development environment (IDE) of choice and sharable across testing platforms.

Teams use different tools across their development environment. You want to have a single performance engineering toolset that works across all IDEs, protocols, and CI/CD environments. Here, integrating your performance engineering toolset across all combinations of environments is vital in saving time and effort. To do this, you want to choose tools that provide a wide variety of integrations, including being fully compatible with open source. If your environment has multiple CI/CD platforms (for example, Jenkins, Azure DevOps, and TeamCity), you can still use a single toolset. Having a single tool that supports testing and analytics across all your environments breaks down the tools’ silos and removes roadblocks to collaboration.

Automating test and execution analysis is another way to make performance engineering more efficient. One factor in accomplishing this is to provide a toolset that centrally automates test execution and analyzes the results, reducing duplication of effort across teams. Teams can improve performance using a single result set to determine any problem’s root cause and avoid passing defects downstream.

Enabling Continuous Testing and Feedback

Change is constant in software development, production, and post-production. Something is constantly changing in customer functionality, scaling, or security. For testing to be most effective, it needs to monitor the effects of changes on performance as close to real-time as feasible. Shift-left testing does this for the development teams, while shift-right testing enables you to monitor real-time engagement with customers. Together they provide a framework for continuous testing and gathering information.

To move from continuous testing to performance engineering, you need an infrastructure that seamlessly takes in testing data and provides the analytics to diagnose performance defects. With the analysis in hand, teams need to work collaboratively to determine the defect’s root cause. Team analysis is essential to performance engineering because even an error detected in production may have originated during code development. This analysis is necessary for correcting the current issue and engineering changes to the process that protects against repeating the problem.

Collaboration requires various people with differing responsibilities and toolsets to work together, analyzing the data and all phases of testing, from script development to implementation automation. This scenario requires a toolset integrated across tools and job responsibilities. A performance engineering tool needs unified functionality with various interfaces and integrations to accommodate multiple roles.


Properly tooled and implemented continuous testing can lead to performance engineering. It requires tooling on the performance testing side that integrates into various job functions, tools, environments, and production environments to provide a consolidated view of the analytics.

The LoadRunner suite of tools enables your teams to work collaboratively and ensure performance across your entire software lifecycle. For more information, explore Micro Focus.

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