Multiple sectors of the economy are transforming artificial intelligence (AI) and machine learning (ML) and influencing many areas of our everyday lives.

Workplaces such as banking, healthcare, retail, education, and technology use AI to automate activities, minimize costs, and make decisions guided by data. AI drives TV and movie recommendations, personal digital assistants, surveillance cameras, and home automation in our homes.

AI in Software Testing:

AI tries to make testing smarter and more effective in software testing. To simplify and optimize testing, AI and machine learning apply reasoning and issue solving. AI can minimize time-consuming manual testing in software testing, meaning teams can concentrate on more challenging activities, such as designing creative new features.

Checking that these types of systems are usable, secure, stable, effective, available, and resilient is becoming more and more important as AI continues to permeate our environment. In other words, AI needs analysis. Sadly, we have not seen many developments in the area of testing AI-based systems.

On the bright side, the potential for AI and ML to cross the gap between human and machine-driven testing capabilities is recognized by researchers and practitioners. As a result, AI-powered automated testing tools are being developed by several organizations.

There has been an increase in the number of providers providing AI-driven testing services since 2014. The majority of these tool vendors are start-up companies targeting mobile device system-level research, and the topic is creating some much-needed industry buzz.


The initial step is to decide whether you are involved or an end user of these tools in the design and implementation of AI-driven systems.

There needs to be a clear understanding of AI and machine learning for an engineer who constructs these types of systems. MIT and Stanford University also have introductory courses on machine learning that are freely available.


AI is altering the research environment now. And while we do not know precisely what the future of software testing holds, by stabilizing and scaling test automation to mature our processes, we can plan for it.

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