From e-business and data analytics to mobility and social media, the digital transformation of everything has been disruptive. Further, as consumers grow more comfortable using digital tools, they expect them to work quickly and flawlessly. Consumers want applications that do more, in less time. Meanwhile, the global enterprise needs software at the very core of their business for commerce and communication. These challenges converge to create a continuous demand for new digital applications, based on faster software development using new tools and more effective quality assurance testing.
Answering these challenges requires Agile software testing solutions. Software quality, then, has emerged as a lever of differentiation in the marketplace…*
*The 2017 World Quality Report
Improving the user experience is now the goal of all software development. The new methodologies used to deliver that experience are Agile software development and DevOps, which are changing the way developers and testers think and work. These methodologies empower continuous software improvement. Yet, adapting to these process changes is challenging.
Today, more stakeholders are involved, many of whom are non-technical. Their primary concerns are better market alignment and supporting the needs of the business strategy. Agile software development/DevOps require these and other stakeholders to come to terms with continuous improvement and collaborative thinking. As Agile software development/DevOps gain popularity, software quality assurance and testing must keep pace with the changes.
Big data is growing as e-commerce and the value of data analytics accelerate demand for more data. Clearly, analyzing large amounts of data provides insight that would otherwise be unavailable or overlooked. Thus, big data analytics will soon be embedded into almost every application despite the challenge of scaling Agile development and the testing environment to manage big data. Meanwhile, the requirements of regulatory compliance and data security are especially problematic for big data environments.
In terms of software development, managing and controlling quality assurance in a big data environment means dealing with unstructured, ever changing data sets that are hard to replicate for testing QA.
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