Importance of Application Performance Testing in the Cloud

Conventional application performance testing using on-premise testing environments barely suffice in addressing the testing challenges facing software development organizations. Progressive development teams releasing iterative software updates at the ultra-fast pace of Agile and DevOps must ensure the application performs better with every update. QA professionals using conventional means of performance testing are naturally forced to employ additional hardware and software resources as they struggle to keep up with the rapid pace of development. Resource limitations ultimately bottleneck testing efficiencies and productivity, forcing testing teams to cut corners in testing each application update against the desired standards of performance. The application eventually suffers from costly software defects that emerge too late into the development lifecycle for developers to issue a viable fix. The defects ultimately translate into the poor end-user experience and costly performance lapses compromising the product’s value, market share and competitive strength.

[See Also: Cloudify Your Network Using Open Source]

In this context, the cloud has enabled a paradigm shift in performance & software testing services that facilitates agile development sprints while ensuring that each iterative sprint progresses toward better end-products in terms of quality, performance, and value.

Value of Testing in the Cloud

The prevalence of cloud computing has shifted software consumption trends against on-premise deployment and installation of software products. Testing the applications delivered across the cloud require advanced capabilities as QA teams analyze a diverse set of performance metrics, circumstances, and objectives. For instance, ideal performance testing environments must replicate a range of IT infrastructure setups including hardware, software, and middleware network to test from the perspective of a diverse user base.

The ability to scale testing resources and virtualize environments on a whim facilitates continuous testing as part of the Agile and DevOps SDLC philosophy. QA professionals are empowered with the necessary tooling, hardware and software resources within a visualized environment to conduct tests in parallel, simultaneously and synchronized with fast development sprints. The testing environment is set up in seconds and minutes instead of days and weeks within affordable service subscription tiers. Achieving the same capability with conventional testing practices requires high CapEx and OpEx into purchasing, deploying and maintaining testing resources utilized on a limited basis as needed.

[See Also: Cloud Computing Requires Systems Thinking]

Third party software testing services further facilitate ideal simulation of real-world scenarios such as traffic spikes and load variation. Performance testing in the cloud can be customized and automated to replace sluggish, error-prone and costly manual testing practices compromising software quality and failing to identify defects early on during the development lifecycle until it’s simply too late and non-viable to address these concerns. A myriad of cloud testing tools available in the market enables testing efficiencies, automation, and productivity to reduce testing times and promote faster development.

Bottom Line

Without cloud capabilities, organizations end up investing significant resources to replicate production-grade testing environments and can hardly match a fraction of vast performance circumstances in the real world.  Although cloud computing presents unprecedented value propositions for QA professionals to improve on software testing capabilities, security and cost concerns inherent with cloud services in general demand a strategic approach toward cloud testing. Investing in cloud testing resources is only the first step toward improving testing results and productivity. Analyzing the right set of metrics is critical to maximizing the potential of continuous testing conducted in parallel across multiple iterations and scenarios.

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Harsh Raval

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