The technology industry is stronger and dynamic than ever, thanks to Amazon, Uber, Facebook, Google. Price wars do not drive success for them but the convenience they bring for millions of lives. Software is driving organizational success with AI enhancing overall development practices. AI is the ‘not-so’ secret ingredient for smart software development practices. Agile and DevOps are speeding, organizations are using AI to automate the development process. QA and testing–the two areas where companies are experimenting and implementing use cases to automate manual processes.
2018-19, World Quality Report, “57% of the respondents said they had projects involving the use of AI for QA and testing, already in place or planned for the next 12 months.” The numbers show that organizations are looking to adopt AI in creating self-operating test systems.
One-size doesn’t fit all
The very first challenge for organizations is to achieve a desired level of automation and speed with the existing legacy applications and new application structures. The second challenge is “one-size doesn’t fit all”. Enterprises require unique testing frameworks and architectures for each application to test them optimally. The third challenge is an ever-changing UI for applications because of the flexibility provided by Agile and DevOps. This requires a continuous change of automated testing scripts.
AI-driven testing platforms have potential to create smart testing landscape for a dynamic, modern and ever-changing applications. Such AI tools support business objectives to deliver quality applications at a faster rate.
Translating the potential into reality
Organizations surpassed experimentation phase for AI testing use cases. They realized AI has the potential to transform slow, manual and error prone testing processes. 2018-19 World Quality Report, states that 36% respondents were using AI for predictive analytics in testing and 35% said they were using AI for descriptive analytics in testing. As organizations continue to mature in predictive and descriptive AI tools, we will see more use cases with the advanced AI tools and technologies.
The future testing platforms will have solutions such as script less test automation, test data and environment management, security and performance testing to achieve a complete end to end AI-driven testing. They will provide intelligence, analytics and availability of dashboards to understand how testing life cycle is improving and gaps to improve it further. Soon, it will be possible to analyze log files and test even before code is ready for testing.
Such AI testing tools will help in correcting code during the development phase and suggesting course corrections in development stage to avoid potential pitfalls. These course correction solutions will create a knowledge repository for organizations, for a quick fix. This helps in parallel execution of tests and code development to make deliveries even faster.
Leveraging AI will be the new normal
With AI tools and accelerators available in the market, organizations leveraging AI will be the new normal. The debate around identifying right processes for AI will shift to leveraging AI for maximum manual processes. This requires enterprises to go under a rapid cultural change.
Organizations need to consider Systems under test (SUT) and Applications under test (AUT) requirements by going beyond the requirement documents. Teams leading the effort need to look at the business proposals, product case studies, high-level business problems and detailed system and design requirements for selecting right tools and technologies. This includes reviewing data requirements, process and environment requirements and framework and tool requirements. Organizations need to tie their AI testing tools to gain desired outcomes for measurable results. Features to look out for when organizations choose AI-driven QA and testing tools–
The path towards AI
As AI finds its way into the development activities, organizations need to look for various ways to consume AI in different engineering practices. The AI driven testing platforms will save time and efforts towards the redundant and manual tasks, allowing teams to explore other areas of software testing. The best possible way of leveraging AI to automate QA and testing is adoption of right tools and re-skilling to work in collaboration with AI systems. In the long run, AI is not about investments in tools and technologies but adopting it to sustain, scale and deliver quality products to the customers.