Given the speed with which companies are churning digitally innovative apps every day, we are embarking on an automated future. From self-driving cars to smarter recommendations on Netflix, automation and artificial intelligence have transformed the world as we know it. According to Ray Kurzweil, Director of Engineering at Google, by the year 2029, computers and machines will be smarter than humans. The recent advancement in technology has enabled computers to not only solve complex and nonlinear problems but with the integration of artificial intelligence, computers are now able to complete tasks that require logic and even predict the future behavior of consumers based on past trends.
The role of automation is of great import in software application development and UX design; with an automated development and testing approach, the organizations would be able to, create compelling products, streamline the workflow and operations of the team, and most importantly, optimize the process of giving design feedback.
Need for Automated Testing for Applications
Apart from saving the time and energy of humans by doing the tedious and monotonous tasks, incorporating automation in the development cycle smooths the organizational processes. So, it is no surprise that the idea of automated testing and processes is gaining traction in the information technology industry, especially when it comes to the performance and security of applications. The reliability and stability of applications is contingent on the effective testing; from malfunctioning of features and bugs in the app to low system performance, there are numerous quality and performance issues that cannot be detected and fixed without the involvement of an efficient automation testing company.
Why You Should Opt for Automated UX Testing?
Taking a user-oriented approach in testing and development is the key to ascertain a good user experience that will keep them coming back for more. With the introduction of various platforms, numerous devices and interfaces, different network connections, and screen sizes, keeping up the user expectations and demands is a daunting task for the automation testing companies and development teams. However, the agile methodologies which enable the teams to have shorter sprints and test cycles, and facilitates the process of continuous testing, along with the automation of testing processes, not only improve the quality of testing, but it also keeps the end-user satisfied. Some of the predominant benefits of automation of user experience testing comprise of;
Efficient allocation of resources – manual testing of software application generally requires more human resources and time, while automated testing eliminates the need for most of the resources which can be utilized for other high priority tasks. Even though it requires some effort and time to set up and ensure the smooth running of processes, in the beginning, the automated tests can run practically unattended once they are up and running, so that the testers and developers only need to analyze the results after its completion.
Reduction of costs – although the upfront costs of automated testing and the cost of assimilation are high initially, it does not come as a surprise that employment of an automation testing company would save the organization from huge and unnecessary costs in the future. With the automation of processes, the testers are able to detect an issue in the early phases of the development which prevents it from snowballing into a much larger problem in the future, thereby saving the development team huge amount of time, effort, and money.
Higher test coverage – automated testing makes it simpler for the teams to generate various different test cases and test multiple scenarios including different operating systems, mobile devices, browsers, connection speeds, and screen resolutions.
Higher return on investment – one of the most favorable aspects of automation of testing for user experience is the attainment of sustainable competitive advantage and creation of value for all the stakeholders involved. The efficiency in the operations leads to a direct increase in the productivity of the teams, which results in profit maximization.
Performance testing – for accurate and reliable stress tests and performance tests, it is absolutely crucial for the company to automate their testing process mainly because automated tests can simulate hundreds and thousands of virtual users, and simulation at such a large scale is not possible with manual testing.
How Artificial Intelligence Enhances User Experience
From one perspective, the ultimate goal of both artificial intelligence and user experience is the same: to predict user behavior and anticipate their actions well in advance. From predictive analysis to machine learning systems, there are more ways than one with which artificial intelligence enhances the user experience.
Intelligent algorithms – Creating an application to provide a better user experience boils down to the smart algorithms of artificial intelligence. By processing data from multiple platforms about site visitors and their behavior, a system backed by artificial intelligence is able to develop performance metrics for testing the application in order to optimize the user experience as per the derived insights.
Quantitative usability testing – Owing to the colossal volume of data available, it is virtually impossible to process it without automation and the use of artificial intelligence. With the use of machine learning mechanisms, the testers and developers can track and analyze the vast amount of pivotal user experience metrics, like; user location, session time, session length, features or categories viewed, what kind of device the users are using when they are on the website or app, pages visited, bounce rates, user flow, and exit pages.
Elimination of bias in testing – The one-sided approach to A/B testing, the challenge of remaining objective in split testing, and the influence of manmade justifications and reasoning, are some of the daunting obstacles faced by testers which can be easily eliminated with the integration of artificial intelligence. Unlike manual testers, artificial intelligence is contingent on hard numbers and impartiality when analyzing data, and uses a binary approach which is reflected in the outcome as well.
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