To keep pace with constantly evolving business requirements that are brought by market dynamics, competitive pressures and business turbulence, enterprises across the globe have embraced DevOps/ DevSecOps to ensure faster software delivery, shifted IT infrastructure to the cloud, and embraced test automation. Although, test automation has speed up the project delivery cycles, there are still some bottlenecks – identification of the test scenarios, Develop test automation scripts, test coverage and maintenance of the scripts that need to be addressed. The solution to all these challenges is AI-powered test automation.
In this article, we’ll discuss challenges related to traditional test automation and how artificial intelligence (AI) can help in addressing those challenges.
Challenges with Traditional Test Automation
#1 Challenge: Bridging skill gap
One of the biggest drawbacks with traditional test automation is that tools are developed with a programming mindset. It means that testers should learn how to code if they want to create test scripts. But we understand that testers are not programmers and coding is a big problem for them. Furthermore, testing across enterprise apps is conventionally executed by business users. So, finding the right test automation tool with shortest learning path is a challenging task.
#2 Challenge: Test Authoring
Starting from scratch when it comes to test authoring is something that testers hate the most. Whenever a new test automation project is initiated, regardless of having reusable components, testers need to start writing a lot of similar code again. A repetitive task that can be frustrating even for the most patient testers.
#3 Challenge: Test Prioritizing
What to test and what not to test is still a million dollar question. Test engineers still need to pick up regression tests cases based on their industry experience, and often, they make guesses when it comes to regression testing. The downside of this approach is that either over testing or under testing. Over testing can consume lot of time without offering much of test coverage while under testing will expose your business to unnecessary risks.
# 4 Challenge: Test Maintenance
In traditional test automation, testers often struggle while maintaining the test automation scripts with each sprint. Test script maintenance can be very challenging task in a scenario where enterprise application vendors like Oracle are rolling out quarterly updates. Oracle’s automation scripts are hard to maintain because of presence of dynamic object property (Name, ID, Xpath, CSS etc.).Imagine how much time and efforts are required when you’ve to execute Oracle Cloud testing 4 times a year.
Addressing Challenges in Traditional Test Automation with AI
No Code Test Automation: Rather than relying on code-based test automation tools, you need to bring in zero code test automation platforms. With script-less testing tools, testers neither have to spend too much time in learning programming to run the tool nor in automating their regression tests by writing code.
Autonomous Test Script Generation: AI’s subset Natural Language Processing (NLP) based test automation tools can perfectly handle challenges related to test authoring. NLP allows testers to write test cases in plain natural language with minimum or no amount of training (eg. English). Ai-powered platforms automatically generate test automation scripts by reading the test steps. Tests created in natural language can easily be understandable for users at all levels – Business Analysts, Manual Testers, QA Managers, stakeholders, etc. All can participate in writing tests to ensure adequate test coverage.
Risk-based Testing: AI-powered test automation platforms perfectly addresses the challenges related to inadequate coverage. AI-powered testing frameworks cut unnecessary time and complexity from testing by enabling testing for “most-at-risk” objects. Every time QA teams don’t need to run the entire test suite for even a small change in the application.
Self-healing: As a perfect solution to major problem of test maintenance, AI-powered self-healing capabilities automatically detect changes across object property (Name, ID, Xpath, CSS etc.) without requiring human intervention. For instance, test automation platforms with self-healing capabilities perfectly cater to Oracle Cloud testing as scripts break due to change in dynamic elements. By autonomously identifying the change made to an element locator (ID), or a screen/flow, self-healing based platforms fix automation scripts to avoid test failures, flaky/brittle tests.
Enterprises that want to stay ahead in the game with digital transformation, need to understand that traditional test automation is holding them back. They need to transform their testing by investing in zero code, Ai-powered test automation platforms. The immediate ROI for this would be shorter release cycles,adequate test coverage, and more stable builds.