What is Performance Engineering & Why We Need It
November 15, 2022

What Is Performance Engineering?

Continuous Testing

As a software-oriented approach, performance engineering models system behavior early during the Software Development Lifecycle (SDLC), to allow engineering teams to evaluate performance trade-offs as early as possible.

In this blog, we will discuss the different phases of performance engineering; and the frameworks and evaluation models that can help your organization adopt various performance engineering processes and practices. We will also discuss the best practices for continuous performance testing in DevOps environments within the scope of a performance engineering framework.

Table of Contents:

What is Performance Engineering?

Performance engineering is a systematic approach for developing software applications to ensure they meet the expected performance objectives. It is a discipline that is focused on the architectural design, coding and implementation choices that engineers make, including their technologies, practices, processes and frameworks. 

These are optimized using quantitative methods and mathematical models that evaluate and predict performance characteristics.

As part of the performance engineering process, performance tests are shifted left and conducted to recognize and correct bottlenecks and other performance issues, generate testing data to improve system modeling, and further improve the performance prediction capabilities of the system model. Then, the results are monitored and the process is optimized.

In modern engineering organizations, the responsibilities of performance engineering fall within the scope of three modern engineering roles: Developers, DevOps and QA.

A Methodology for Performance Engineering

To achieve performance, performance engineering teams can choose from a variety of performance engineering frameworks. These frameworks provide recipes for implementing performance engineering in organizations. Let us review some of the most important processes a performance engineering framework comprises in a client-server architecture:

1. Understanding the Environment

  • Identifying the components responsible for critical and/or important business transactions. Capturing transaction data and evaluating it based on relevant business metrics such as processing time, number of queries, resource consumption, and other key metrics.
  • Identify the cost and technology constraints associated with various infrastructure and application components. It may be possible that the service levels for critical transactions may be most impacted by a limited set of underlying hardware resources.
  • Determining service levels and infrastructure.

2. Characterizing the Workload

  • Identifying workload intensity and estimating service demands for architectural components.

3. Establishing a Performance Model

  • Develop a performance model of the complete end-to-end system. This model should accurately represent the system and its behavior in response to real-world traffic and query workloads.

4. Executing the Performance Model

  • Measure the model performance throughout various traffic patterns. Testing the software and monitoring performance metrics such as response time and throughput per transaction, API calls, hardware, and software components.
  • Identifying the performance bottlenecks with the goal of optimizing for overall system performance. Optimization can include a risk-averse strategy: setting up the system to maintain service levels above a certain base case or optimizing against peak workload demands.

5. Assessing the Performance

  • Comparing the performance levels with the required service levels. 
  • Identifying the components where transactions take the longest to process or are performed inadequately across other performance metrics.
  • Based on the evaluation, evaluating how changes in the software architecture, workload distribution, and resource allocation can potentially address the performance gaps.

6. Improving in Iterations

  • Making changes iteratively based on the presented assessments, but in line with business objectives and requirements.

This procedure should be repeated and evaluated continuously while updating performance models, based on the real-world performance of the application in a production environment. The ongoing optimization enables faster release cycles, higher quality, and improved end-user experience.

For example, performance engineering will enable DevOps teams to answer the following questions:

  • How can the application become more scalable and dependable?
  • How can the infrastructure recover quickly from failure incidents and perform optimally for a global user base?

Getting Started: A Performance Engineering Maturity Model

The guidelines and framework described above specify what to do in a performance engineering job. But knowing where to start with performance engineers requires a readiness assessment of the organization. This can be determined by using a maturity model.

Traditional maturity models such as the Capability Maturity Model (CMM) offer a starting point but are focused on entire software engineering processes. Let’s review a similar framework, known as the Performance Engineering Maturity Model (PEMM) that relies on CMM principles, but is designed specifically for organizations evaluating their performance engineering readiness.

Each level of the PEMM defines the key criteria across several aspects that can be used from the perspective of project and process management. In this blog, we will limit the discussion to performance engineering capabilities and processes as it relates to continuous testing in DevOps environments. The maturity levels of PEMM are as follows:

Level 1: Uncoordinated Practices

  • Lack of structured mechanism for performance engineering at the organizational or team level.
  • Use cases are highly dependent on individuals in QA and testing teams who may take initiatives for introducing performance testing of non-functional requirements early during the SDLC pipeline.

Level 2: Consideration

  • Subprocesses of performance engineering exist but in an uncoordinated structure.
  • Service providers for the necessary resources are available.
  • Lack of a process description to guide end-to-end performance engineering.

Level 3: Definition

  • A standardized performance engineering process is available but not necessarily implemented. Necessary technologies are available and deployed.
  • Resource metrics are evaluated and standardized.
  • Customer requirements are closely evaluated and noted in the technology SLA from service providers.

Level 4: Integrated and Approved

  • Performance engineering is an integral component of the software development program.
  • Relevant stakeholders utilize performance engineering metrics and develop performance models within considerations of privacy, security, and organizational policy guidelines.
  • Domain-specific use cases of testing are defined.
  • The performance engineering program follows a structured approach to performance testing in line with DevOps principles.

Level 5: Optimization

  • The highest level of performance engineering maturity is achieved.
  • Multiple business applications and use cases leverage performance engineering early in the SDLC to help achieve business objectives.
  • The performance engineering pipeline is adaptable and optimized based on feedback from performance models and real-world application performance metrics.

These levels can be used for continuous comparison of efforts that go into developing system and performance models, identifying key metrics, and optimizing the most impactful test cases. A maturity model assessment can also be used as a readiness assessment tool to plan for resource investments that go into various stages of the performance engineering frameworks.

Performance Engineering and Performance Testing

An important discipline in performance engineering is performance testing. How can engineering teams make the most out of their testing efforts within a performance engineering framework? The following general best practices for continuous testing in a performance engineering setting can be most impactful:

1. Determine Your Use Cases and KPIs

Before running a high volume of performance test cases, prioritize and focus on cases that improve code quality and mitigate risks for your most important use cases, early during the SDLC. You can use prioritization methodologies such as the Utilization Saturation Errors (USE) model for performance feedback loops.

It is important to communicate these use cases and metrics to all stakeholders involved, developers, DevOps, and QA, and ensure everyone has the necessary tooling and capabilities.

2. Automate

Automation is key for successful performance testing implementation. Use automation tools for running the tests. Make scripting easy for cross-functional teams. Employ simplified automation tools that allow for no-code/low-code or GUI-based testing. Finally, integrate with CI/CD pipelines.

You may want to consider independent pipelines for specific load testing cases, like certain edge cases.

3. Simplify Reporting and Analysis

Your reporting and analysis will help you A) Monitor the success of your tests and, consequently, your code. B) Optimize your future tests. Make sure that performance data analysis can be easily consumed by cross-functional teams, as well as decision-makers who are not actively involved in performance testing. 

Bottom Line

In the context of modern SDLC methodologies, Performance Engineering follows the DevOps principles of shift-left and continuous testing; collaboration between Developers, DevOps, and QA; and the use of automation to improve software quality and shorten the release cycles.

In order to achieve these goals, your organization requires the right set of automation tools and continuous testing tools such as the BlazeMeter platform that offers end-to-end testing capabilities within the scope of any performance engineering framework. So start your performance testing today!

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