The Imperative for Load Testing Modern Software

The High Bar Set for Load Testing Today

What are the criteria for a capable load testing tool that meets the needs of an app today, living in the Cloud, built with a microservices architecture, linking to external resource?  What should it include to ensure your dev team delivers a reliable, high-performance app that meets users’ expectations?  Following are the seven requirements we see setting the bar:

  1. It has to be current, covering the latest technologies.  That means every relevant browser and every web-services based app.  It needs to be able to create and execute tests that run across all these dimensions.  It needs to ramp up the load in a public or private cloud, or any grid of systems from a simple, non-code oriented, interface regardless of vendor. And then ramp down, at the end of a simulated surge.
  1. It must test load across the entire technology stack. It should be able to run tests at the browser level, gathering all browser UX timing, as well as at the API and microservices levels when required.  And it should be able to achieve both in parallel.
  1. It must be highly productive and easy for the QA team to use.  It should require no coding, with rapid manual test creation and/or AI-driven autonomous test creation.  It should require minimal script maintenance.  It should support automatic test initiation using a variety of CI tools so as to easily integrate into a company’s continuous testing process. And it shouldn’t require QA to rely on a separate team or a separate load testing tool, creating a bottleneck delaying the run of what should be on-demand load tests.
  1. It must be scalable, supporting load tests from 100 to 3,000,000 users.  It should automatically launch as many test nodes as needed, and then tear them back down to keep cost in check. Furthermore, it should be able to scale UX or API-level tests at varying ramp up speeds, thus allowing the load-tested application’s load balancers to respond.
  1. It must give you visibility into the load testing process.  Test engineers should be able to access the tool via a web browser (thin client).  It should also fully integrate with a variety of application performance monitoring (APM) systems, aligning timings from various sources to present a complete picture of your application’s performance.
  1. It must provide true response timing. This requires built-in server monitoring, plus application performance monitoring (APM) integrations. Plus, it should be able to run UX and API level tests in parallel to gather true user timing, not just server timing. True user timing measurement is crucial, as the response time users experience in their app can differ greatly from what server-level only tests reveal.
  2. It must produce actionable analytics.  It should be able to produce a scalability report that illustrates actual versus expected transactions per second. Such analytics let you quickly assess where systems begin to suffer so you can adjust the communication and architecture as needed.

Leaving the Past Behind

Load testing has always leaned heavily upon automation to ensure that software has the ability to scale and handle any strains it may encounter.  However, traditional offerings are simply too narrow to meet the bar set out in our seven criteria above.  Not to mention that these legacy automation systems make a QA team’s job unnecessarily more complex and challenging.  This can lead to higher labor costs, less productivity, and the risk of increased performance failures that harm the business and the brand.

To function successfully going forward and keep pace with the ever-rising scale and new challenges, load testing must be easy to execute, not a luxury. And it must not be limited by a stand-alone tool that can only be utilized by a specialized team that is often overbooked.

AI-augmented QA and Meeting the Performance Challenge

Fortunately, modern load testing solutions like AIQ incorporate AI to solve the problems currently encroaching upon load testing. Using advanced machine learning algorithms tuned to QA practices, Appvance’s AI is the technological leap that software teams need to succeed in load testing modern applications.  AIQ is currently the only offering on the market that embodies all seven of the hallmarks outlined above, being the very definition of a superior load testing automation platform.  Only an AI-powered solution can provide the power and rigor needed to address the complexity of today’s application architectures and dependence on systems beyond our internal control, like third-party applications and integrations or content delivery networks (CDNs). 

Beyond these considerations, AIQ enables load testing to be conducted not just by the same testing team, but also using the same test assets.  By using functional and AI-generated test scripts for load testing, the unified testing model removes the duplicate effort of creating separate load test scripts from functional test scripts. As a result, adopting AIQ can reduce the labor required to conduct load testing by up to 90% compared to using legacy automation systems like LoadRunner and JMeter.  Such new methodologies save costs, improve labor productivity, and help teams keep pace with their DevOps processes and release goals.

If your team is ready to dramatically upgrade its load testing capabilities, as well as increasing the ROI of your software testing tools, contact us now for a meeting. We would be happy to show you how AIQ is the only platform prepared to load test your applications while meeting the high bar of the requirements that all teams should be striving for, both now and into the future. Request a meeting today!

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