How Artificial Intelligence is Transforming Software Testing

When Apexon was founded in the early 2000’s, the founders set out to radically improve the quality and reliability of digital products.  Their expertise gave them insight into how to combine the right people, skills, and tools in order to deliver the best results.  So it shouldn’t come as a surprise that we evaluate each new technology in our market and formulate an opinion – just like our view that human touch is absolutely critical in AI-based quality tools.

The use of AI in software testing is now an increasingly common theme.  Many people in the software industry recognize that it is an inevitability that AI will have an increased role in testing.  But the reality is that AI won’t fix everything – at least not now and not on its own.  To deliver results, AI needs to be in the hands of testing experts that can harness its capabilities, within the framework of a process that aligns to business outcomes.  This is what we call Intelligent Testing – and it is going to transform software testing for the better.

The truth is that while Agile environments have delivered vast improvements in software development, time and again we see recurring problems in testing.  We are seeing uncertainty in prioritization of test cases, too much time spent clarifying and communicating test requirements, a huge backlog of test cases and rising test environment costs.  We are seeing high test script maintenance costs due to frequent changes to applications.  There is too much time spent on application fixes, inefficient use of resources executing similar testing steps.  There is time wasted conducting testing that won’t impact quality or coverage, difficulty accessing and managing test data and a lack of expertise with the latest technologies and tools that provide predictive insights.  Do I still have your attention?  I could go on, but let’s just say there is a lot of room for optimization.

This is why Intelligent Testing exists – to completely optimize every phase of the testing cycle, through people, processes and tools.

We have seen that Intelligent Testing can increase agility by 25-35 percent.  That means savings in the total effort required.  Those freed-up resources can then be used instead for more strategic or impactful work.  Quality can be increased by 30-40 percent.  With increased test optimization, firms can get the latest and most needed software features into the hands of customers quicker.  And it can increase efficiency between 20 and 40 percent.  With greater operational efficiency, developers can have a better understanding of what is critical to performance, functionality and CX.

These are all great-sounding headline figures, but how do they become a reality?

• Strategic guidance to set proper and consistent quality goals and ensure all customer-facing applications meet performance standards
• Identification of key test cases and risk-based test selections
• Expertise identifying and implementing the right technologies and tools to support the on-time delivery of quality releases without vulnerabilities
• Efficient and comprehensive test execution
• Proven processes to advance problem-solving and test optimization from the first step
• Expertise to apply automation as broadly as possible
• AI accelerators for rapid product delivery with enhanced coverage and quality confidence
• Monitoring and measurement of test results with a traceability loop to enhance business initiatives
• Efficient maintenance of the health of test cases
• Ability to leverage test data analytics to refine testing processes on a constant basis.

If you are one of the many enterprises that sees something familiar in the list of Agile testing problems, get in touch with us through the contact form below and let’s talk about how we can radically transform your testing operation, for the better.