July 6, 2021 3 mins read

Top 7 Trends Shaping the Future of Testing Automation

 

In the age of digital transformations, efficient software testing is pivotal to the smooth functioning of apps. Testing automation plays a major role in this regard as it allows developers to overcome the shortcomings of manual testing, while also broadening the scope for more layered tests.

Furthermore, automated testing empowers businesses by enhancing their software quality and making the testing process faster, more reliable, and resource-efficient. Quality at speed has emerged as a guiding principle for modern ventures and testing automation as the path to achieving the same.

Testing automation isn’t a new phenomenon and has become quite popular with innovations such as Selenium. However, present and ongoing developments in AI and Machine Learning are initiating significant transformations by opening up several unprecedented avenues in testing automation.

In this blog, we discuss the top 7 test automation trends that prevailed throughout 2019 and the new automation testing trends that are expected to become even stronger in the coming years.

DevOps & Continuous Testing
Out of the current trends in automation testing, the growing affinity for digital transformations and agile processes over the last decade has brought speed and collaborations to the center of modern organizational methodologies. This has led to the adoption of DevOps-based software development with an immense focus on Continuous Integration (CI) and Continuous Delivery (CD).

Backed by this phenomenon, Continuous Testing has emerged as a principal factor guiding the testing automation industry. It enhances the quality assurance of DevOps teams and involves end-to-end functional testing automation and cross-functional integration. The success of DevOps teams very much depends upon the thoroughness of automated testing performed at various levels of the CI pipeline.

Easier Test Script Maintenance
The creation of test scripts and, more importantly, their maintenance has been the major struggles of the testing automation industry over time. Working on tight schedules and with immense data load, developers often forget to update the automated test scripts in sync with the changed test element ID.

This leads to failed test scripts, resulting in a significant loss of resources and time. However, with the use of AI-based tools, there has been a trending shift from this problematic tendency. With the runtime self-updating ability, these tools have ruled out the need to change test scripts for each new interface manually.

Self Healing with Machine Learning
Every test run produces large amounts of data that include logs, HTML data, browser output, images, and so on. Recent innovations in AI have allowed developers to collect this data efficiently and leverage it for training machine learning algorithms.

This has made it easier to develop intelligent testing systems that can flag deviations from the expected behavior. Consequently, testing automation is now becoming more accurate as developers can predict issues even before they occur, enabling them to make corrective alterations at run-time.

Enhanced Test Analytics
In testing automation, it’s not enough to know that there is some error. To adequately solve the issue at hand, the developer must also analyze the error, determine its cause and precise location, identify its dependencies, and more.
If done manually, this process is immensely resource-intensive. This is especially true for large enterprises that need to test applications involving multiple user interfaces, versions, or a high degree of complexity.

Even in this regard, AI and ML-based solutions have been proving increasingly helpful. Now, it is possible to develop analytics systems that perform the tedious task of browsing millions of server logs. Apart from the increased scope and more detailed error analysis, this reduces the developer’s involvement in low-skill, repetitive tasks, so that they can focus on innovations.

Emerging Test Automation Vendors
With the advent of Selenium – an open-source test automation tool – solution vendors in this field had lost a majority of their foothold in the industry. However, alongside the rise of AI-based testing automation solutions, a new category of vendors are emerging. This rise is backed by the fact that the existing Selenium-based solutions often do not cater to certain project-specific needs of developers.

Primarily, this is due to the lack of open-source solutions catering to the emerging method of testing automation. As a result, there has been a rise in the initial costs involved in the adoption of AI-based testing automation. However, this is compensated by the overall decline in process costs in the long run and the benefits accrued from resource-efficient testing. Besides, this will significantly enhance the scope of automation testing in the future.

The Return of ‘Record’ and ‘Playback’
In a conservative movement, much-dreaded methods such as Record and Playback have been re-emerging in the testing automation scene. Most present-day testing automation solutions have these functionalities.

However, the new avatars of these functionalities are significantly different as now they have been coupled with ML-based solutions, ensuring enhanced run-time reliability of test processes.

Mergers and Collaborations
This final trend on our list of top 7 trends in testing automation is a combination of the other latest trends in automation testing discussed so far.

As a result of the rising demand for AI-based testing automation, an increasing number of organizations are striving to develop end-to-end solutions. However, individual businesses lack the full extent of the resources required for this purpose.

This has led to several mergers and acquisitions in the recent past, including the one involving Perforce and Perfecto Mobile. This trend is not only expected to continue but also gain strength as the demand for comprehensive solutions steadily rises, backed by innovations in this field.

Conclusion
As evident by the trends discussed in this article, the future scope for automation testing is being shaped by innovations in AI and Machine Learning. As processes become more intelligent, the testing becomes more efficient and reliable with automation testing tools in demand for the future.

Developers now have more time and resources to focus on innovations instead of repetitive tasks. They can make better decisions, empowered by the emergence of advanced test analytics data and predictability. Businesses, on the other hand, benefit from seamless, resource-efficient processes while significantly improving the quality of their products faster than ever before, resulting in faster time to market and better ROI. Lastly, better quality products mean that customers are more satisfied with enhanced experiences.

 

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