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Monday 20 August 2018
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Pitfalls to Adopting and or Integrating AI into your Automated Testing

Pitfalls to Adopting and or Integrating AI into your Automated Testing

Many organizations focus on the advantages of using AI for automated testing processes, but what about the pitfalls associated with it? While there are many reasons to consider integrating AI into your current workflow, it’s important to also consider the challenges associated with this technology.

How can it help

AI can speed up your testing processes by handling the repetitive and mundane parts, as well as “learning” from the data that’s fed into it. It’s very efficient at what it does within the constraints of its algorithms and data models. It helps reduce errors since it has a consistent decision-making process, and it’s wholly logical in the way that it operates. Finally, the scale at which the AI tests goes far beyond what human testers are capable of. It can look at and compare massive data sets, perform a large number of operations and otherwise have a workload that a person wouldn’t be able to handle. All of these benefits add up to a faster time to market and quicker turnaround times in your software development cycles.

How to get started

You can use an established AI-drive automated testing platform to see whether it fits with your requirements. Alternatively, if you work with a software development partner, they may already have this technology integrated into their own automated testing processes. They can also help you deploy the solution that makes the most sense for your development projects. Talk with your testers to see where your current processes fall short, so you can find the AI testing solution that addresses these pain points and makes everything more efficient.

What are the potential challenges with using AI with your automated testing?

It may be tempting to offload a lot of the testing process to AI. However, there are a few challenges that you need to keep in mind when you’re making this decision.

AI is not a replacement for human staff. Each has their strengths and weaknesses. The AI is great for making logical connections and processing a lot of data, but human staff can make intuitive leaps and think outside the box in a way that the AI is unable to. The best approach is to use AI as a complement for your testing staff. It augments their capabilities, offloads tasks that make more sense for AI to take over, and fits seamlessly into the workflow. The AI is only as good as the algorithms and the data you feed it, so there is a learning curve in figuring out how to make it work effectively for your organization.

It’s also expensive to implement. While the ROI is worth it in the long run, you may have trouble convincing upper management that they should make this investment. You also need to consider the price of hiring specialists to maintain AI-based systems. SaaS solutions can help you control these costs, but may not necessarily be the right choice for your organization.

Is it worth the time to try to move forward with using AI in automated testing? 

AI brings a lot of value to automated testing processes. As long as you consider the disadvantages alongside the advantages, you can move forward with a full understanding of what this entails. A software development partner can make this process smoother, so consider bringing in a specialized provider to guide you through this step.




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