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Micro FocusMicro Focus surveyed 1,700 CIOs and other senior professionals on QA and testing – here are the main takeaways.

The 2018-19 Micro Focus World Quality Report found a complete change in perspective in regards to QA and testing objectives. This year, the top priority of respondents was to “ensure end-user satisfaction”. A criterion that was almost unthinkable 10 years ago, now has become concrete – much is due to the shift to Agile & DevOps, the cloud wave, and machine learning assistance.

Micro FocusSource: 2018-19 Micro Focus World Quality Report

An increased end-user satisfaction means a higher customer centricity in organizations – which drives digital and QA transformation. Companies are looking for solutions that can deliver speed, convenience (seamless UI), and security to their QA and IT environment. Companies are seeking to optimize the software release process, to then continuously improve the end-user experience. There are some major trends that are responsible for this shift, the main ones include cloud, machine learning (ML), Agile & DevOps, and Automation.

Cloud & security of a SaaS testing platform

According to the survey, today, an average of 76% of applications across organizations are based in the cloud. Software as a service (SaaS) enables companies to easily and rapidly adopt solutions and integrate them into their IT and testing environment. The survey found out that managers fear the idea of shifting to the cloud. Perhaps they are concerned that the shift to cloud computing naturally brings to surface issues on security – the handling of data by third parties, the mobile network access, deployment on external servers, and so on.

Ironically, most companies still lack a clear strategy for security validation. Both SaaS platforms and IT or QA managers have responsibilities in architecting barriers to prevent hacks and data exposure – read how it can be done.

ML in QA testing & end-user experience

Micro Focus believes that machine learning is going to be one of the biggest trends in QA testing in the next coming years. ML is forcing companies to reconsider how they are using their testing resources. They are now considering a solution with a more efficient way to leverage manual testers’ knowledge.

A big bunch of the correspondents (57%) also said they had projects involving the use of AI for QA and testing already in place – or planned – for the next 12 months. Although AI or ML are still new territories, recent developments have already vastly diminished maintenance time and costs of QA. ML algorithms designed to maintain tests by overcoming changes in the app are enabling manual testers to focus their business knowledge on projects to improve – rather than fix – an application. ML, thus, has a direct impact on end-user experience.

Micro Focus

Source: 2018-19 Micro Focus World Quality Report

Agile & DevOps in QA testing – speed over quality?

It is safe to say that Agile and DevOps are used in almost all organizations (99% of respondents), even if only on specific projects. The issue here is that the need for fast releases has surpassed focus on quality – which is a self-destructive practice. To avoid this problematic overlapping, DevOps, QA teams and managers must take advantage of tools specifically designed to integrate testing and development.

Test automation is essential

Much of the tools that integrate testing and development can be categorized as automation platforms – which have been around for some quiet time. Nevertheless, test automation practices according to the survey, are very low (between 14-18% – see graph below). The reason is that many organizations struggle when trying to automate their QA and testing process – software releases are very frequent and finding a robust solution that keeps up with the fast pace is seen to be very difficult for companies.

Micro FocusSource: 2018-19 Micro Focus World Quality Report

61% of respondents find it difficult to automate because their applications change too much with every release.

Micro Focus

Source: 2018-19 Micro Focus World Quality Report

“Moving forward, organizations will need to move toward higher levels of end-to-end testing automation. The test automation solutions must be enhanced with smart cognitive solutions that will enable the self-running and self-adaptive test platforms”.

In other words, the future of testing relies on the implementation of codeless automated tools focused on UI, with the aid of ML assistance for a higher and more solid test coverage. This will, in turn, boost manual testers’ capabilities and directly affect the quality of the software.

Future of Testing

The future of testing does not rely on taking manual testers out of the equation. It is based on the premise of avoiding human intervention wherever is possible to let in the room for innovation; reducing costs, increasing quality, achieving a faster time to market, and finally improving end-user satisfaction. The way to achieve all of these goals is through test automation. Even further, test automation must be equipped with smart cognitive solutions that will enable self-running and self-adaptive test platforms. All in all, testing professionals can look forward to exciting developments.

 

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