AI-Driven software testing
Today we are looking at another type of testing which is gaining traction fast: AI-Driven software testing. In other words, the use of Artificial Intelligence (AI) and Machine Learning (ML) to improve the overall testing process.
What is it?
Essentially, it is when AI is used to inject more automation into development and testing. This is achieved via the application of reasoning, problem solving and then also through ML. AI can successfully generate test cases, identify defects, and predict testing outcomes. It can then also reduce the amount of mundane and tedious tasks involved, meaning that its application in software testing is growing.
AI is also being used more in line with a general trend of more organisations now exploring how AI can help them. This is due to the fact that AI requires data, computing power and algorithms. Algorithms have been around for a while but big data and power have now arrived at a point where AI is a distinct reality.
What are the benefits?
Increased test coverage: This can be achieved by the automatic generation of test cases covering different scenarios and edge cases.
Improved efficiency: Achieved via the automation of repetitive tasks and a reduction in human intervention.
Increased accuracy: AI algorithms can identify defects with a higher degree of accuracy than human beings.
Predictive testing: This can allow software testing teams to identify potential issues before they occur.
Self-healing: AI-Driven testing can automatically detect and then fix issues without a need for human intervention.
Adaptability: This type of testing can adapt to changing requirements and learn from previous experiences, thus increasing efficiency.
It is worth noting that AI-Driven software testing is still an emerging field. It is also not a replacement for human testers. Instead, it is another tool which can help to improve the overall testing process. It should ideally be used alongside other testing methodologies including manual testing and exploratory testing.