In the previous blog, we had covered the applications of AI in e-commerce testing and how it is used to enhance the testing process through various means. Continuing from where we left, let’s explore in this blog a range of obstacles faced by e-commerce players that hold them back to adopt AI in software testing.
Today, e-commerce businesses need faster deployments but with inadequate infrastructures. Hence, AI is being leveraged to deliver what is quintessential in the e-commerce testing and development cycle. As a matter of fact, leveraging AI during testing can help in generating and optimizing the test cases, prioritizing and automating the testing activities and overall enhancing UI testing and other analytical tasks. However, there are multiple challenges that major e-commerce players (eg Magento commerce cloud, Salesforce commerce cloud) may face while leveraging AI for testing. Let’s explore a few of them.
- Generation of a large amount of data- Today’s e-commerce software development & testing pipeline generates massive amounts of data from multiple channels. Data is the fuel for the new AI engines. The machine learning process is completely dependent on the data thus leading to a large volume of the dataset like data for test, environment, build, coverage and production. AI model test scenarios should be equipped to identify and remove human bias which often becomes part of training and testing datasets. It’s not just the ability to work with it but also the quality and quantity of it available that is tough for AI to be adopted in this sphere.
- The other big one is identifying the exact use cases and generation of the corresponding test cases.
- When implementing AI in testing, it is important to consider and simulate a tester’s perspective else the testers might end up performing limitless testing. They would have to face challenges like exhaustive testing, testing the areas of the application for which the business has very little importance, fewer priority defects, duplicate defects .This, in turn, would cause delays in going live at the need of the hour.
- The verification and validation of the behavior of the e-commerce site/platform based on the input data is another challenge.
- Skills shortage– A software tester has experience in how to test software using different guidelines and test approaches. For professional and structured testing, there are standard certifications that are around for a long time, such as TMap and ISTQB. Other certifications in the fields of Agile, Requirements Engineering, and Mobile testing are also valuable to a tester. But now the skill set will need to further grow as AI plays an increasingly major role. Testing using AI embraces machine learning, mathematics & statistics, Big Data analysis and much more. The team needs to have many of these skills (discussed further below in detail) to successfully carry out their AI-related testing tasks.
- Testing the site/platform for functionality, performance, scalability, stability, security, and much more for it to be successful is no less a burden.
- It would be best to work with technology partners who offer skilled AI/ML teams to implement the AI-enabled platforms effectively for testing and development needs.
- Reskilling and retraining testers through workforce transformation programs are the need of the hour and would help combat challenges. Unless organizations take active steps to retrain their testers and develop new skills, it could pose a critical bottleneck holding back the progress of AI-enabled testing.
- Designing learning models, used by AI in such a way that it will depict & simulate human behavior
The need for qualified professionals for AI testing in 2019 will increase as more and more organizations experiment with “intelligent QA. Clearly there is an enthusiasm and excitement about implementing AI technologies and solutions, but their actual application in testing is still emerging. Nevertheless, AI is here to stay and its importance will continue to grow in the upcoming years.
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