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All the apps we use daily go through rigorous quality assessment (QA), which is done through a series of analytical tests. 

Software development and testing are regarded by many professionals in the industry as being in constant metamorphosis. When we say technology undergoes rapid changes, for developers and testers this may mean minutes. 

(Also Read: AI Gets a Common Sense Boost) 

Analytical testing methods are moving in a more responsive direction– feedback is provided at the earliest and every stage of the software development lifecycle (SDLC). More time and cost-efficient methods are being brought to the fore of QA, especially as the world turns more reliant on the connectivity of all the devices we use. 

DevOps Integration

Development and operations are two distinct departments that in the old days worked separately. Before, software development moved more linearly as it moved from the drawing board to the final product. Today, developers and testers are taking more efficient approaches by integrating input from these two teams to streamline processes and catch issues at every stage, rather than at the end of each. 

Shift-left/shift-right testing

More developers are taking on a shift-left approach when it comes to analytical testing. Shift-left takes the concept of working backward on a project and pushing testing at the earliest stages of development rather than at the end. Testing earlier allows developers and analysts to identify issues and course correct accordingly. This approach reduces costs and shapes better results. 

On the other hand, shift-right testing focuses on the real-life performance of software, when it’s out in the world. This is important also, but only because feedback should be continuous for improvement, and not so for replacement or recall of a product. 

AI and Machine Learning

The integration of AI and machine learning tools in quality assessment is instrumental in accelerating processes. The advantages are plentiful: automating repetitive tasks, predicting problems, and revamping the execution of test cases. Machine learning is valuable when it comes to monitoring algorithms and patterns. Both tools improve the testing process two-fold: it saves time and it allows developers to become more responsive to changes that arise. 

Intelligent Test Scripting

In the QA arena, automation testing or test automation is the practice of using software tools and scripts to automate the execution of tests. Very recently, more intelligent test scripting has become a trend. Bolstered by AI, the generation and maintenance of test scripts are automated, instead of these being done manually.  Intelligent test scripting makes automation testing more responsive and resilient. 

IoT Testing

IoT and mobile testing are two of the biggest trends in analytical testing today.

Our devices are increasingly becoming more interconnected—we use our smartphones to control other appliances, and these appliances may also be connected to other devices. The digital ecosystems we have in our homes and workplaces undergo testing not only for their specific functions but also for how they communicate and interact with the devices they’re connected to. Developers test for compatibility between devices, make sure operating connected devices feel seamless and work as one, and ensure security as the devices exchange data. 

Performance Engineering

A great user experience is the mark of success for software. The more natural it feels to use an app, the better. Encountering glitches and bugs, no matter how small easily crushes the experience of using software. Developers are attuned to user experience and continually seek solutions not only to improve it but anticipate features we may not even know we need. 

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