ManageEngine, the enterprise IT management division of Zoho Corporation, announced the integration between Endpoint Central, its flagship unified endpoint management solution, and Check Point's Harmony Mobile, a leading mobile threat defense solution, to help IT security teams automate the remediation of mobile threats.
The future of DevOps is bright and the opportunities to utilize cutting edge artificial intelligence (AI) and machine learning (ML) applications of these technologies will only further enhance its adoption. Recently, Tricentis in partnership with Techstrong Group surveyed over 2,600 DevOps practitioners around the world to examine how AI was being leveraged across various DevOps practices. Across those surveyed, most respondents said they already see great results from AI-powered DevOps practices — a step in the right direction towards unlocking AI's greatest potential: testing.
In the survey, almost 70% of respondents called out that AI will have the most impact in DevOps around testing. In the past, testing has been a repeated pain point for development teams. From the volume of data needed to perform these outcomes and the difficulty to scale automation to complexity required, testing has needed a solution to keep pace with what DevOps practitioners require from it. Now with greater AI functionality, testers can better identify risk, accelerate the authoring of tests, and repair broken tests faster.
How AI Enhances Test Automation
Software testing operates either as functional, where the program enables users to properly execute tasks, or non-functional, where the application is secure, fast and stable. As stated, respondents of the survey were most excited about the use of AI in functional testing, with unit and UI testing selected next.
Because of functional testing's many nuances, AI is set to thrive in such a complex environment. Functional testing is open-ended and numerous variations of testing scenarios must happen in order to curate proper testing coverage. In this, AI can be used to manage these requirements while providing new insights alongside data. Also, AI can help flag production signals that may not be identified in a non AI-infused testing environment.
In addition to functional testing, respondents across the survey said AI also had great promise for UI testing. Testers know how intensive this type of testing can be, as it still operates as a manual process. AI's greatest asset to this kind of testing is that it can mimic the actions of end-users and through automation, an AI-powered application can adjust the variations and nuances in real time. With automation inserted into the testing environment, it also boosts the build process by increasing the efficiency of repetitive tasks, in turn, increasing the effectiveness.
Adopting AI-Enhanced Test Automation in your DevOps Practices
If you are an organization that has reached a point where it makes sense to begin integrating AI-enhanced test automation into your DevOps practices, where do practitioners start? In my opinion, it begins with the big picture. In order to successfully integrate these practices, you must identify the specific use case you want to focus on. Is it a larger business problem? It's important to remember AI and ML can add business value, but they don't solve every problem across the life cycle of your software.
After identifying your goals and quantifiable success metrics, organizations should focus on areas they can utilize automation, especially when it comes to repetitive testing, such as regression and unit testing. I see many organizations begin with implementing continuous integration and delivery. Once an organization takes these steps, it will start to amass a large cache of results and artifacts generated from running the test cases, which AI can mine to increase test stability and identify recurring problems. The more you automate, the more you'll be able to train the AI/ML model and get faster and more accurate results.
What Does AI-Enhanced Test Automation Look Like in Practice?
Consider any organization that has 20 applications to manage. With the volume of these applications, it's necessary to conduct mass UI testing and the tests conducted must be stable. One of the biggest challenges continually identified in UI testing sits with locating various web elements — most people start with manual script-based automation, but it is time-consuming to create, as well as to maintain. Employing AI to identify these elements saves time and drastically cuts down on testing maintenance.
In the survey, respondents were asked how they currently use AI to augment their testing procedures. Close to half are accelerating their automated test cases, while 44% want to focus their testing on the areas of high risk. 50% reduced test case maintenance with self-healing and 43% use AI to identify the root cause of failed tests. 31% said AI offers insights into test process improvements and 34% use AI to help identify tests to run based on changes within the application.
What's Next for AI in Quality Engineering?
In the near future, AI will become critically essential and more accessible to the testing process. As tooling continues to mature, AI-enhanced products will reduce the need for specialized skills, allowing testers to be more involved with strategic projects, not intensive manual work. DevOps is ripe for AI exploration with testing will become integral for the development of new practices.