AI-Enabled Test Automation Maintenance Leveraging Self-Healing

Test automation teams have scaled many heights in automating enterprise applications at large. However, they run into challenges at execution time when applications undergo up-dates. A minor revision in the application user interface can result in test failures and become roadblocks for the test automation execution. Changing dynamics of end-user expectations are driving organizations to accelerate code promotion to production. Agile model of development is being endorsed more than ever, thus leading to an absolute requirement to implement measures to reduce test automation maintenance efforts. A self-healing mechanism to auto fix the scripts by identifying errors and triggers would help the testing team minimize maintenance efforts. This also calls for an AI-enabled solution that can learn the nuances of errors to fix them and reduce manual interventions.
 

Typical conditions that make the automation scripts fail?

Multiple reasons cause an automation project to fail like non-standard coding, unreasonable expectations, lack of understanding of the functionality, ergonomic tools selection etc. However, if we consider primary contributors for an automation script failure will be invalid locators for application elements, network blips during script execution and failures in the system infrastructure. Amongst the faults mentioned above, the network and infrastructure parameters can be addressed by defined processes as they can be predicted based on Analytics or rule books.

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