One of the critical reasons for software release delays is the time consumed in maintaining the failed test cases and inability of current testing system to self heal the test automation. Test automation is proving ineffective and outdated (Automation is always out of date) due to the time and cost involved in maintaining these failed test cases. As per Webomates’ study, the cost of software test automation is only 20% in the setup phase, and the cost of maintenance of the software test automation is 80% of the overall cost.
The broken test cases and scripts disrupt the software release cycle, and analyzing the root cause of broken test scripts is even more time-consuming. According to a study by IBM, searching, managing, maintaining, and generating test data encompass 30%-60% of the tester’s time. AiHealing bridges the gap as it not only identifies the changes in the test cases and automation scripts but also adapts them in real time. AiHealing is playing a vital role in faster software release.
What is AiHealing?
AiHealing is Webomates’ patented technology that efficiently reduces the time and cost of maintaining failed test cases. AiHealing is a 3-step process depicted in the figure below:
Photo of Self Healing Test Automation by Webomates Inc
Step1: Identifying the Defects
The AI system analyzes the pass/fail report and other execution data and identifies false failures. AI system takes two category inputs:
Input: Test Automation execution data: The most apparent entity, in this case, is the Pass/Fail report and exception data results for this execution run. However, other data such as locators, test data inputs, timeouts, console logs, and network logs can also help in AI decision-making.
Input: Build Release Notes: This is the text that describes the features in the build. These are the User Stories stored in a system like Atlassian’s JIRA, which contains the text describing the features or bug fixes that are in a particular build.
Step 2: Root Cause Identification
Once the AI Analyzer detects the required changes, it moves to the next step of identifying the Root Cause for the failure of Automation Scripts.
Script Change: From a process perspective, the AI system analyzes the pass/fail report and other execution data and identifies false failures. Then it recognizes issues such as application changes, features added, and newly added test cases and does a root causes analysis. The issues could be due to locator changes, script errors, timeout errors, and feature changes. These items alone cause 60% of the false positives. Our AI system not only identifies test data changes but also end-to-end flow changes. Additionally, we have release notes/user stories that help identify the test cases that need healing. After our AI Defect predictor recognizes the root cause, it proposes the next course of action, i.e. healing the test case. For More Information Visit - www.webomates.com
Test Scripts once created are automatically executed while doing any new releases. Test Automation often breaks down in case of any new feature release or any changes in locator
AiHealing bridges the gap as it not only identifies the changes in the test cases and automation scripts but also adapts them in real time. Webomates’ AiHealing is a process of Self Healing Test Automation and thus helping in faster releases