Artificial intelligence is not only transforming the way we live; it is transforming how we work — especially in the software development world. Today, the technology is being implemented within development teams and tools as a way to catch bugs earlier and automatically assess and correct code.
According to John Bates, CEO of the test automation solution provider TestPlant, in this new AI first world, the software testing industry — which was once perceived as a compliance task — will be more important than ever.
We spoke to Bates about how AI will disrupt the software testing world, and how it can provide a high quality digital experience.
SD Times: Why is artificial intelligence such a hot topic today?
Bates: What people really want are intelligent systems. Take analytics for example. Everybody loves the idea, but how do you make decisions on data that is changing very quickly? How can you tell it to look for patterns if you don’t know what to look for?
People want systems that figure it out for you because the signal to noise ratio is so poor. Intelligent systems can make sense of the vast quantities of data and actually make recommendations for you about what to do as well as look at your results and tell you how to refine what you are doing.
That is why I think artificial intelligence is so popular. It radically improves your time to market, radically improves the value of your human resources, and can radically improve profitability if you do it right.
How can the software testing industry use AI to drive their businesses?
People need to look at all sorts of different aspects and places where you can use different AI algorithms to help make software more productive and smarter. First, look at how you can use AI intelligently. You can have an algorithm that can operate like a user actually accessing the automation. The second part is figuring out with pathways within an application you should exercise. Having a smart algorithm can help figure out the pathways to exercise as well as find the maximize number of bugs and problems. Thirdly, you can you use the results from the system to refine what you are doing and learn from it.
Those are some ideas, but every software area has different ways you can apply it.
What are the benefits of applying AI to software testing?
There’s a lot of talk currently about test automation. However, in reality, we’ve only automated one key element: test execution. AI and analytics will be the catalysts to deliver true test automation that recommends the tests to carry out, learns continuously, enabling it to predict business impacts enabling dev teams to fix issues before they occur. This will help teams to keep up with user expectations and the pace of DevOps something they are struggling with today.
What is a software tester’s role in an AI world?
The software tester still has a very key role. It is absolutely important to understand this isn’t a robot coming to take your job. This is a smart assistant. The software tester is still responsible for modeling the workflow that you have, and setting up the environment and tooling. Also, they are the ones reviewing the results and looking at the interface to provide recommendations from the systems and the feedback to the development team and to the business.
What should testers be aware of when adding AI to their workflows?
In every area, when people look at getting stated there is always a fear of new stuff and making the wrong decisions. First, it is important to be aware that these techniques are becoming possible, and explore what this actually means and how this could be productive.Often the biggest barrier to adoption is the human sociological barrier to either not knowing it is possible, and being afraid. I would encourage embracing and experimenting. Pick a project you are working on, and see how productive this could make you.
How do you predict AI will transform how we develop and deliver software?
AI is transforming the way we work and live and it will have a significant impact on how software is developed. AI algorithms will intelligently navigate applications to predict where the quality issues are most likely to be, and identify the key data correlations that will help developers resolve issues quickly. This will ultimately result in happier customers, which will dramatically improve conversion and retention rates resulting in more revenue. A win win for everybody.