Testing plays a crucial role in maintaining the quality of enterprise software. And as the world’s most popular packaged applications–like Oracle, Salesforce, and Workday–release software more frequently, enterprises need a testing process that keeps up, and enables them to make the most of the softwares’ new features and security enhancements as quickly as possible.
Testing is meant to ensure that these application updates don’t have bugs that prevent users from effectively using the applications your organization has spent hundreds of thousands, or even millions, of dollars on.
But most organizations are testing manually, and their employees spend countless hours performing manual testing tasks that don’t actually reduce risk. Here are a few of the main problems with manual testing:
- Manual testers need to perform hours of repetitive tasks. Because these tasks are labor-intensive, boring, and time-consuming, these tests are prone to human error.
- Deciding what to test is often a guess-and-check exercise that requires hours of meetings discussing business processes. As a result of this, most organizations either test too much–wasting precious time and money–or test too little, which exposes their applications to downtime risk.
- Since most modern enterprise applications are dynamic in nature, it’s practically impossible to reuse tests for regression testing purposes. Because of this, test maintenance becomes a major burden that often requires hundreds of hours of work for testers.
- With manual testing processes, organizations often don’t discover bugs until late into the testing process. When bugs are discovered late, they cost 15X more to fix than when they’re caught early, according to IBM.
- As a wise person once said, time is money. And by its very nature, manual testing is time consuming, as it requires employees–who have actual jobs to do–to spend hours of time manually making sure that processes work as intended.
These challenges can be addressed by incorporating artificial intelligence in test automation.
Before discussing how, let’s discuss what artificial intelligence is!
Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans. AI provides systems with the ability to learn from data or experience and continuously improve themselves, and can be utilized to automate hours of repetitive work.
Artificial Intelligence and test automation
Self-healing: AI-based self-healing capabilities can save enterprises countless hours of test maintenance work. Whenever a test script breaks from a change in a dynamic object property, self-healing capabilities can accommodate the change, and \fix issues automatically without human intervention. And as we mentioned, because most of today’s packaged apps are dynamic in nature, there are typically hundreds of changes with each update.
By largely reducing test maintenance efforts, your QA teams can focus on more high-value activities.
Test mining: By utilizing machine AI-driven test mining techniques, organizations can understand the exact business processes of their employees, and then prioritize testing on the most critical ones. Said another way, AI ensures that your continuous testing program has adequate test coverage, which in turn ensures your applications won’t break.
No code test automation: AI simplifies test script creation by allowing business users to write test cases in plain English, as Machine Learning engines reads the test steps and generates automated tests. This can reduce the test script design time and effort by over 70%.
Test and Business Process Modeling: With AI , the entire business process documentation and discovery lifecycle can be automated. Machine learning engines pull data from all of the various applications involved in executing a business process, from beginning to end, and offer a bird’s-eye view of the entire end-to-end process. By using AI, most organizations reduce test and business process discovery from months-long endeavors to days.
Test Data Management: AI can greatly reduce the time taken to collect and organize test data. By mining business processes, machine learning agents can mine Master Data Details such as Chart of Account, Employee, Customers, Item, Supplier, Procure to Pay, Order to Cash, and more, which can reduce QA teams’ data collection efforts by up to 40%. The quality of data procured during this process is also much higher, since it’s performed by a computer.
Eliminate Repetitive Tasks: AI-powered test automation frameworks replace manual testing by automating most of the repetitive — but necessary — tasks used in regression testing. Because this work doesn’t require the time or attention of employees, they can instead focus on how to make the most of their packaged applications’ new features and functionalities. Which enables your enterprise to stay competitive.