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Enterprise-Grade QA on a Budget

By Gareth Smith

Gareth Smith

Gareth Smith - 21 May 2019

We operate in a continuous delivery world in which a seamless customer experience is paramount. Regardless of whether you’re a global Fortune 500 organization or a fast-growing startup, failing to deliver a digital experience that delights your users is a critical mistake you can’t afford to make.

A chief challenge compounding today’s continuous delivery expectation is the growing amount of testing that has to be carried out. In the not-too-distant past, companies controlled all of their software, available on a single platform to a similar type of user with one uniform release cycle. Today’s landscape is vastly different, with websites and apps relying on a mix of modules and services under the control of various vendors, all with independent release cycles, in a heterogenous platform environment with a wide range of user types.

While all companies are united in their need to overcome these challenges to produce a high-quality user experience, how they go about testing their websites and apps can differ significantly. After all, a multinational corporation has much deeper pockets and greater resources than a startup.

So how can you scale up quality assurance when you can’t scale up the budget? It sounds like a tricky problem, but it’s actually quite simple when you deploy intelligent, automated testing.

Budget-conscious organizations can adopt a model-based approach to testing, in which a digital twin of the application or system under test is built. This allows them to test through the eyes of their users by defining all the possible user journeys and ensuring the technology performs optimally at every turn. AI-driven testing includes:

  • Regression packs: The definition of mission critical end-to-end tests that must pass prior to shipping the product. AI and machine learning can then be applied to the information gleaned from these fixed tests to identify other test cases to ensure the product delights in the field.
  • Bug hunting: Advanced machine learning can correlate common factors and attributes of historic defects to identify new attributes that indicate the highest likelihood of discovering new defects.
  • Coverage analysis: Analyzing where you have historically been in the model and also providing a balanced view to ensure as much test coverage as possible.
  • Real user journeys: Monitoring how users are actually interacting with the technology and feeding those insights back into the model.

This functionality ensures that the small amount of time it takes to build a model is rewarded by an enormous number of possible test cases. As a result, even the leanest of testing teams can optimize their testing environment and address continuous delivery requirements.

Small, fast-growing organizations may face an uphill battle when competing with more established companies in a variety of areas, but testing does not have to be one of them. For more on achieving enterprise-grade QA without scaling up the budget, register for our webinar on the topic here.