For most enterprise companies, software testing is time consuming and costly. With increased complexity of software applications, many companies have turned to test automation to alleviate these frustrations. The mission is commendable—to accelerate the testing cycles and to get their products to market ahead of the competition.

However, despite their best efforts, enterprises that have adopted test automation continue to struggle with inefficiencies. Common inefficiencies include the need for continued manual intervention across defect analysis, fix prioritization, and automating regression tests after each sprint. It often seems that an increase in efficiency in certain areas leads to new inefficiencies in other areas.

While these issues may appear to be an acceptable cost of automation, next-generation test automation solutions can help solve this dilemma. As an example, Aquila Test Automation was developed precisely to tackle the inefficiencies of legacy test automation solutions. Aquila comes with artificial intelligence (AI) and machine learning (ML) built into the platform from the ground up, enabling development teams to shift their attention away from mundane tasks and towards strategic decision making. With Aquila’s next-generation AI test automation platform, enterprise firms can now optimize their holistic testing plan without being weighed down by time-consuming and costly manual processes.

Next-generation AI-powered test automation platforms should be built with AI from the ground up, as opposed to being patched into a legacy platform. These AI test automation platforms help engineering teams better understand the following:


What To Test

By highlighting components and workflows not covered in test cases.


When To Test

By prioritizing component dependences after software upgrades.


How To Test

By optimizing test coverage and testing sequences based on priority.

Next-generation test automation platforms are able to analyze past test results and patterns, interdependencies between features and components, and even the testing workflows of different users and roles. Through advanced Al/ML models, platforms like Aquila can propose the optimal test plan and test cases to be tested, thereby helping testing teams decide what to test. By recommending what to test, Aquila can improve test case prioritization and overall test coverage. Most importantly, Aquila eliminates potential testing blindspots and helps enhance overall product quality with every test run.

After knowing what to test, the next step is determining when to test. Understanding the dependencies between numerous test cases can be a logistical headache, and even worse, often reside in the heads of key stakeholders. A next-generation test automation platform like Aquila overcomes these issues by analyzing the interdependencies of test cases and software updates of integrated systems such as Salesforce patches. Through AI/ML models, Aquila recommends the optimal time to test, along with a specific set of prioritized test scenarios to balance test coverage and test cycle times. If specific areas of concern are addressed early, Aquila can save overall testing times by recommending fixes to these problematic areas before resuming or rerunning the test cycle.

Even after knowing what and when to test, the final challenge is optimizing the sequence of tests – in other words, how to test. In addition to the continuous stream of new features and bug fixes, there are endless combinations and permutations of test sequences that testing teams face. Aquila solves this problem by applying its machine learning algorithms to optimize the sequencing of test cases across business scenarios and feature updates. As new software updates get released, Aquila will continue to learn from changes to user workflows and adapt future test runs accordingly. Knowing how to test can eliminate much of the software testing bottlenecks that often lead to delays and production code rollbacks.

While test automation is a step in the right direction for most companies, artificial intelligence and machine learning can unlock next-level efficiencies. Aquila is an example of a next-generation test automation platform that can help enterprise companies accelerate their testing journey from initially being reactive, progressing to being proactive, and ultimately to being prescriptive. In the midst of ever-changing customer requirements, increased system integrations, and cutthroat competition, Aquila can help software development teams ship better products while reducing overall test cycle times. The result of faster time to market and enhanced product quality is often what sets industry leaders apart from the pack.


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