3 Main Challenges of AI in Software Testing
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Data Availability
A vast and structured data set is the main precondition to using AI in software testing. If the AI lacks data for analysis, the results may be unreliable. In this respect, AI testing resembles statistics.
Heavy Computational Load
AI implementation can be expensive, as it needs powerful hardware to operate. For example, some AI models from Facebook and Google are economically impractical for this reason. Fortunately, there are some affordable tools we’ll mention later.
If you plan to implement AI, prepare to invest in the most advanced Graphical Processing Units, analyze the operations with memory consumption estimation tools, and adopt a hybrid cloud infrastructure. These will help to lower investment resources.
Lack of Generality
AI-based tools are currently good at solving a specific task but lack generality, given their limited application scenarios. Therefore, the choice of the proper tool may take a long time.
When choosing an AI testing tool, consider several parameters. Identify the testing requirements as functional, performance, security, and usability testing have their peculiarities. Also, you need to keep in mind integration capabilities, ease of use, and vendor reputation.
To make the process of selecting an AI tool easier for you, we will describe the best examples of artificial intelligence software here: AI-Based Test Automation Tools