Involvement of Testers in AIOps Implementation
The pace of technological innovation and its rising complexity has placed a significant strain on IT Operations. Managing and servicing a range of new systems and devices constantly and staying abreast of the ever-evolving enterprise technology toolkit in a complex and dynamic business landscape keeps IT operations teams awake at night. However, AIOps relieve IT teams of certain pressures and streamlines IT operations.
What Is AIOps?
AIOps is essentially an IT operations strategy that leverages technologies like machine learning and data analytics to automate decision-making and solve IT issues. AIOps stands for the use of Artificial Intelligence in IT operations to:
- Detect and remediate outages.
- Monitor availability and performance.
- Facilitate IT service management.
The Importance of Test Automation in AIOps
Solutions powered by AIOps get their data from various resources and give analytics platforms access to this stored data. As such, testing needs to be comprehensive and integrated into the development cycle. Test automation increases the development and deployment velocity and ensures that the tool functions as expected.
For example, chatbots are an instance of AIOps at work. These centrally rely on machine learning. Ensuring adequate chatbot performance demands comprehensive testing to verify the responses. Manually testing these chatbots is effort as well as resource intensive. Extensive testing demands expanding the surface area of testing. This becomes impossible to achieve with manual testing alone.
Given the multiple data points involved and the complex operations AIOps applications perform, it becomes essential to integrate testing into the development cycle. Testing must also become continuous to ensure that all moving parts of the application work as expected and as designed after updates, upgrades, or bug fixes.
The testing complexity also increases with AIOps since this activity has to ensure that the:
- Machine learning analytics work as expected when they turn control over to the system.
- Personnel can audit to see how automated controls are doing their job.
The Role of AI Testers
The AIOps market will reach $14.33 Billion by 2025. As more enterprises look at this technology to optimize operations, testing AIOps implementations thoroughly becomes critical for success.
While test automation is important to launching thoroughly tested AIOps implementations, the role of manual testers cannot be ignored. Since the two primary components of AIOps are machine learning and big data, testing these implementations requires a hybrid approach.
The use of these technologies demands testers to be well-versed with AI and ML terminologies. In addition, they must have a clear idea of all components and steps involved in training the algorithms and driving optimized capabilities.
In essence, testers play an essential role in both the pre and post-implementation stages of AIOps. Here's how.
Pre-Implementation Stage of AIOps
Performance testing of AIOps implementations takes place in the staging environment. Therefore, production-related data is not used at this stage. Testers have to thus script performance testing scenarios and then review them in collaboration with development teams and business users to ensure comprehensive test coverage. This approach also ensures that performance parameters are covered not only from a technology standpoint but also from a business standpoint.
The testing team must also execute comprehensive load tests to ensure that the system performance is even and reliable. Evaluating how system resources perform and determining reasons for scripting failure in the face of higher loads are important testing considerations. These ensure proper resource and infrastructure allocation to drive optimal performance.
Furthermore, testers have to engage with IT teams and share the script execution results and application log files with them. The IT team can then comprehensively analyze various alerts related to the APM monitoring system.
Post-Implementation Stage of AIOps
In the post-implementation stage, testers work with IT and infrastructure teams to identify comprehensive performance testing scenarios. The IT support team creates automated scripts to determine the peak usage of various system resources.
Testers are notified about the dynamic errors at play upon execution of these performance tests. If these errors are managed by the scripts written by the IT team, then they can be sure that the system resources are well managed.
The Importance of AI Test Automation for AIOps
Expanding the testing footprint of AIOps implementations is critical to ensure that these systems deliver their promised benefits. Performance, load, stress tests, and detailed security tests to identify code breaks, bugs, or points of vulnerability are important.
While manual testers cannot be eliminated from this mix, test automation has to do a lot of heavy lifting without increasing testing load and effort. Since these are complex implementations, it makes sense to use AI-powered, comprehensive test automation platforms that ensure end-to-end test automation and deliver greater business assurance.
AI in test automation makes way for better test reusability and increased test coverage owing to self-learning algorithms. Deeper and faster analysis and insights and, consequently, faster decisions are also organic outcomes of using AI to power test automation initiatives.
The Case for Codeless, AI-powered Platforms
AI-powered automation helps testing teams achieve continuous test automation and allows for the expansion of the testing footprint with minimal effort.
Codeless, AI-powered platforms have become even more important to the mix as they allow functional testers and business users to contribute to the testing process. They remove the effort that goes into coding and then reviewing the test automation code. This way, AI-powered test automation platforms drive greater testing productivity. Further, they help enterprises achieve an automation-first QA strategy.
AIOps implementations need thorough testing because these tools need to function as expected when out of the gates. If they don't, they end up diminishing IT and executive trust. Furthermore, since AIOps involves the use of advanced algorithms and AI techniques to analyze big data from various IT and business operations tools and enable higher levels of automation, the implementations must be thoroughly tested to deliver the desired outcomes.
That's where a codeless, AI-powered test automation platform can help. The right platform, like ACCELQ, enables cross-functional ownership of quality where manual testers and automation engineers collaborate without technical barriers. Sign up for a 14-day free trial today!