How Machine Learning and AI Bring a New Dimension to Software Testing
It seems, the software testing industry never sleeps and is always evolving. According to the State of Testing Survey 2017, the future is about automated testing as 62% of respondents believe it will increase in the following years. According to the same report, we can also expect testers spend more time and their resources on testing mobile and hybrid applications, with the time spent on actual development shrinking.
Although huge, none of these factors (testing automation, shorter development cycles or focus on mobile and hybrid applications) are really changing the testing game as much as the emerging technology of machine learning.
Machine learning is being successfully applied now in all walks of life, so the question is, how will machine learning and artificial intelligence influence software testing? Will they actually enhance it?
Read this article to find out how software testing and quality assurance on the whole are evolving in the age of machine learning and artificial intelligence.
The Number One Challenge of Traditional Testing Approaches
Software testing used to be a simple and straightforward task. As long as we knew how the system was to be behaving in use cases, it was relatively easy to enter an input and compare the results with the expectations. A match would mean the test is passed. If there was a mismatch, alarms would go off as we had a potential bug and needed to fix it by starting all over again.
In such a traditional scenario, a tester would look through the checklist to ensure that potential users’ steps and actions were all covered and issues resolved. However, since consumers have become more demanding and less patient in a sense, traditional testing methods often can’t keep up with them.
The main problem lies in the sheer amount of data that testers need to handle in a limited period of time they usually have these days. This alone takes traditional testing methods out of the equation and calls for a more relevant approach. That is, the one powered by artificial intelligence, machine learning and predictive analytics.
Leave it to the Machine: No More Human Intervention (and Error)
Traditional testing techniques still rely on humans to source and analyze data. But let’s just say that humans are not infallible and are quite prone to making poor assumptions.
The less time there is for handling data, the greater the chance that testing will produce skewed results with overlooked bugs in the software. Before you know it, consumers will pick up on these bugs, which usually leads to frustration and undermines the brand’s reputation.
That’s why machine learning, which teaches systems to learn and apply that knowledge in the future, makes software testers come up with more accurate results than traditional testing ever could. Not to mention that the probability of error is not the only thing that gets reduced. The time needed to perform a software test and find a possible bug is also shortened, while the amount of data that needs to be handled can still increase without any strain on the testing team.
Using Predictive Analytics to Foresee Customer Needs
As the market demand grows, businesses need to find a way to be a step ahead of their competitors and be able to predict their consumers’ needs. Predictive analytics plays a key role in quality assurance and software testing as it allows businesses to analyze customer data to better understand (and predict) what new products and features they would want.
On that note, machine learning and predictive analytics go hand in hand in software testing and QA today. They are both necessary for an uninterrupted, shorter testing process that ultimately leads to better user experience.
Where Does Machine Learning Fit in QA and Software Testing?
AI and machine learning are undoubtedly becoming vital components in QA and software testing as well.
Experts are excited about the prospects this could all bring. For example, Managing Director and Testing Services Lead at Accenture for Europe, Africa and Latin America Shalini Chaudhari said in an interview for QA Financial that the reasons AI have taken off are the tremendous data availability due to the IoT breakthrough, and the growing computing power that’s no longer limited to just specialized research institutions.
Conclusion
Machine learning gives testers the opportunity to better understand their customers’ needs and react faster than ever to their changing expectations. In addition, testers now also need to analyze more and more data and they are given less and less time to do that, while their margin of error decreases constantly. Tools such as machine learning and predictive analytics offer a way to address these challenges, either with an in-house teams of well-versed testers, or, if it’s not the case, turning to QA outsourcing. Either way, this approach is set to fill the gaps of traditional testing methods and make the entire process more efficient and relevant to the users’ needs.
