Most common Challenges in Traditional Test Automation | Agilitest blog

Sticking to traditional business methods and failing to innovate has resulted in the collapse of many companies in the past. We all know how a major walkman company was bleeding cash when digitization took over in the music industry. Similarly, a global leader in mobile phones almost ran out of business when it failed to grab a share of the smartphone market. From time to time, such incidents have highlighted the importance of understanding industry trends, and the same applies to the test automation industry.

Test automation promises several benefits, but there are many challenges in the field which have to be overcome using new automation technologies. With newer tools and technologies available in the test automation market, organizations will have to adopt these, or they would lose out on providing faster deliveries, and enhanced user experience to their customers.

To guide you through the challenges in traditional test automation and the solutions available, this article can help you choose the right optimized solution to implement test automation in your project.

So why wait? Let’s start!

Content in the article

• Top Challenges in traditional test automation
• Solutions to address the challenges.
• Conclusion

Challenges in traditional test automation

1. Needs coding knowledge

As traditional test automation does not make use of modern codeless automation tools, it is heavily dependent on the code required to generate test scripts. To execute the project, a tester has to be well-versed in coding practices to build and execute the project. Coding knowledge helps you to write better scripts, run nested loops, debug scripts, and perform a number of such tasks. As a result of this, we need to have testers with good coding skills to execute the automation task.

2. Keeping up with changing requirements

It is indeed difficult to implement test automation when the requirements are frequently changing in the project. Keeping up with these changing requirements manually impedes complete automation. Moreover, maintaining and updating the automation scripts becomes more difficult in such a scenario. This is why we need to have optimized and updated scripts which would execute at a faster rate and provide better test coverage.

‍3. Utilizing reusable assets

Another challenge that we face in traditional test automation is the management of reusable assets. While executing our test scripts, we might come across various scenarios where some elements of a test suite can be reused in another suite, thus helping us save time. However, traditional automation has limited capabilities in identifying such scenarios. This is why we need to look for new intelligent automation solutions which can handle such tasks and yield productive results.

4. Piecemeal automation approach

With shrinking delivery cycles, it is always advisable to try and achieve complete test automation in the project. While it is difficult to do so using the traditional test automation approach, organizations must adopt newer approaches to solve the issue. Adopting a piecemeal approach to test automation can lead to an inefficient solution, causing us time and money.

5. Test script maintenance

Maintaining automated test suites becomes increasingly complicated over time, as test scripts might change several times. If scripts are maintained, it will help the organization in building better and quality software quickly. On the other hand, if we fail in maintaining the scripts on a timely basis it can lead to flakiness and slow test runs.

Solutions to address these challenges

‍1. Use of AI/ML

Artificial Intelligence and Machine learning(AI/ML) has permeated many different fields, including test automation. AI/ML algorithms can be used for different scenarios to overcome the limitations of traditional test automation and further enhance our Automated Testing. The use of AI/ML can also help in achieving complete automation thus preventing human intervention in automation and saving execution time.

AI/ML can be used for:

• Identifying web elements in a better way by defining an efficient and robust locator for web elements.
• Design self-healing scripts by handling unexpected errors.
• Identify repetitive tasks to automate the same.
• Use for smart regression testing.
• Can be used for visual testing.‍

‍2. DevOps implementation

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Read full article here. An article by Priti Gaikwad on our blog.