Best Practices and Key Metrics for Performance Testing
September 03, 2020

Akshaya Choudhary
Cigniti Technologies

Before releasing a software application to the end customers, it must be measured against parameters like robustness, scalability, speed, responsiveness, interoperability, throughput, and stability under different load conditions. This is important as an application with poor usability and functionality will not be accepted by its target customers.

To ensure the fulfillment of these requirements, the application should undergo performance testing under reasonable load conditions. Application performance testing makes sure the software application does not buckle under reasonable load thresholds but meets the pre-defined metrics of performance.


The tech-savvy customers of today expect their software applications to perform every function quickly, accurately, and without any hiccups. Loading speed is an important performance metric to evaluate an application. According to statistics, around 40% of people are expected to abandon a website if it takes more than 3 seconds to load.

Also, a one-second delay in loading a page may end up with 7 percent fewer conversions — a significant number indeed! A performance testing methodology can identify and fix glitches/bottlenecks in the application by generating diagnostic information.

What Are the Types of Performance Testing?

The various types of testing to validate the performance of an application against pre-defined load conditions are as follows:

Load testing: A load performance testing exercise evaluates the response time of the application under a normal workload condition.

Stress testing: Similar to load testing, it evaluates the performance of an application beyond the normal load thresholds. It determines the level of load the application can handle before faltering.

Endurance testing: This type of application load testing measures the performance of an application over a period of time and helps to identify issues like memory leaks. This type of testing is also referred to as soak testing.

Spike testing: It is a type of stress testing wherein the performance of an application is evaluated when subjected to sudden variations in the workload conditions.

Scalability testing: Unlike spike testing, scalability testing involves determining the performance of an application when subjected to a gradual increase in workload.

Volume testing: This type of software application load testing evaluates the performance of an application when faced with a large data volume.

Key Metrics of Performance Testing

Evaluating the performance of a website, web or mobile application against pre-defined load conditions needs the presence of some key metrics shown as under:

Response time: The actual time taken by the application to respond to a specific query.

Average load time: The average time taken by a website or mobile application to load irrespective of the device platforms.

Throughput: Refers to the number of transactions an application can handle in a second.

Peak response time: The duration of the longest response given by the application. If the same is greater than the average load time, then there is a problem to be addressed.

Average latency: Also referred to as wait time, it is the time spent by a request in a queue before getting processed.

Requests per second: Refers to the number of requests handled by the application per second.

Best Practices to Conduct Performance Testing

After choosing the requisite tools, the QA testers can create and execute a performance testing framework as mentioned below:

Test environment: The first step is to create a suitable test environment comprising the hardware, software, and network to execute the test. It is only the right set up reflecting the real-world scenarios that can identify (and mitigate) performance-related issues.

Key performance metrics: Identify the key performance metrics against which measurements have to be taken for performance evaluation. These may include the response time, throughput, load time, concurrent users, error rate, and memory utilization, among others.

Users' perspective: It is important to understand the performance of an application from the users' perspective. So, instead of focusing on factors like server response, find out whether the users shall have a similar experience. This would mean creating a beta version of the product and capturing the users' experience.

Test plan: It includes the schedule, approach, scope, and resources needed to conduct the test. Additionally, it entails the features to be tested, the essential test elements and tasks to be tested, and testers assigned for the role.

Report generation and analysis: The result data are captured and analyzed to identify and fix any possible performance issues.

Retest: For any correctives applied to the application, the same should be retested against similar parameters.

Conclusion

Conducting performance testing can be time-consuming but extremely critical for the success of any application. It lets testers know about the various thresholds the application can handle in terms of traffic, load time, and throughput, among others.

Akshaya Choudhary is Content Marketer at Cigniti Technologies, an Independent Software Testing company
Share this

Industry News

November 21, 2024

Red Hat announced the general availability of Red Hat Enterprise Linux 9.5, the latest version of the enterprise Linux platform.

November 21, 2024

Securiti announced a new solution - Security for AI Copilots in SaaS apps.

November 20, 2024

Spectro Cloud completed a $75 million Series C funding round led by Growth Equity at Goldman Sachs Alternatives with participation from existing Spectro Cloud investors.

November 20, 2024

The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, has announced significant momentum around cloud native training and certifications with the addition of three new project-centric certifications and a series of new Platform Engineering-specific certifications:

November 20, 2024

Red Hat announced the latest version of Red Hat OpenShift AI, its artificial intelligence (AI) and machine learning (ML) platform built on Red Hat OpenShift that enables enterprises to create and deliver AI-enabled applications at scale across the hybrid cloud.

November 20, 2024

Salesforce announced agentic lifecycle management tools to automate Agentforce testing, prototype agents in secure Sandbox environments, and transparently manage usage at scale.

November 19, 2024

OpenText™ unveiled Cloud Editions (CE) 24.4, presenting a suite of transformative advancements in Business Cloud, AI, and Technology to empower the future of AI-driven knowledge work.

November 19, 2024

Red Hat announced new capabilities and enhancements for Red Hat Developer Hub, Red Hat’s enterprise-grade developer portal based on the Backstage project.

November 19, 2024

Pegasystems announced the availability of new AI-driven legacy discovery capabilities in Pega GenAI Blueprint™ to accelerate the daunting task of modernizing legacy systems that hold organizations back.

November 19, 2024

Tricentis launched enhanced cloud capabilities for its flagship solution, Tricentis Tosca, bringing enterprise-ready end-to-end test automation to the cloud.

November 19, 2024

Rafay Systems announced new platform advancements that help enterprises and GPU cloud providers deliver developer-friendly consumption workflows for GPU infrastructure.

November 19, 2024

Apiiro introduced Code-to-Runtime, a new capability using Apiiro’s deep code analysis (DCA) technology to map software architecture and trace all types of software components including APIs, open source software (OSS), and containers to code owners while enriching it with business impact.

November 19, 2024

Zesty announced the launch of Kompass, its automated Kubernetes optimization platform.

November 18, 2024

MacStadium announced the launch of Orka Engine, the latest addition to its Orka product line.