Rachel Garry

AI is already creating a mark for itself in the software testing domain, as it can solve complex testing challenges by incorporating some of the best AI strategies in testing. The aim is to simply make the entire process of software testing highly efficient by churning out faster deliverables with less to no errors and also saving other key resources to a great extent. AI also brings in methods such as problem-solving and reasoning that can be tactfully implemented in the testing process. 

In this article, you will get to know the key factors that can be used to implement AI in software testing.

Following are the five key factors that depict the implementation of AI (Artificial Intelligence) in software testing: 1. User traffic is leveraged: One of the key issues with test management is that equal weight is assigned to each test. This may not be the case always. User error is occurred due to some tests when compared to others because there is a rare usage of certain functions. For instance, thousands of test cases run through a regression kit, each of which may take a few minutes to run. In the above-mentioned scenario, the ProdPerfect tool can be used so that the click data can be assembled and then Machine Learning (ML) can be utilized to ascertain the key user flows that need to be examined. Then, test code can be created to automatically produce a comprehensive test suite. 2. Forecasting and monitoring: For system observability, Splunk is considered to be a great tool. The main features are analysis and log aggregation, apart from the others. A Machine Learning Toolkit (MLTK) is offered by this tool through which standard ML algorithms such as Logistic and Linear Regression, K-means clustering can be configured by users on to the Splunk data. To make this entire process even simpler, MLTK smart assistants were recently unveiled by Splunk. 3. Reviewing the program statically: There are certain specific rules that need to be employed when AI is being implemented in software testing. These rule-based systems gained prominence initially, but, then it did not prove to be affordable. Over a period of time, the situation has changed and now data processing can be commodified and is affordable. In the above mentioned scenario, the Deepcode.ai software tool can be used. Through this tool, symbolic AI is leveraged to perform code reviews and thus adhere to good coding practices. The shortcomings of contamination analysis are justified so that potential attack vectors in the code are extracted. 4. The issue with test implementation: What if the team makes a mistake due to which the deployment needs to be rolled back right away? In this specific scenario, the harness.io tool can be used to solve this issue, where observability data is analyzed from APM tools such as AppDynamics, New Relic and Sumo Logic in order to determine whether there is a requirement for automatic rollback.

5. Analyzing and rectifying errors: There will be instances where testers and developers can write wacky and error-filled test codes. Quality assurance and efficiency are antagonized by wacky test codes. In certain scenarios, identifying the specific placement of the error could be a time-consuming activity. These specific codes can be examined by AI and ML based tools, so that errors can be identified and certain scenarios can be corrected.