The excellence of Artificial Intelligence (AI) has revamped the software testing arena. This is evident with the recent propagation of “AIOps,” which effectively incorporates artificial intelligence across IT operations. On a much broader canvas, we can also view it as the constructive method of using machine learning or data science to resolve IT-related discrepancies.
Presently, artificial intelligence to supervise multiple IT functions, such as performance monitoring, service management, remediating blackouts, etc., is gaining popularity. Therefore, it is slowly becoming imperative to get hold of AIOps for ensuring fast-paced software delivery.
Growing Demand for AIOps
The extensiveness of AIOps as part of the software testing plan is positively responsible at the global level. This is mainly due to this process’s usefulness and its ability to assist the quicker release of complex applications. Many organizations have greatly benefitted as AIOps can handle vast amounts of data without much worry.
Machine learning (ML) and Big Data are seen as the two primary components of AIOps. Therefore, this unites data from monitoring and further assists in automation tasks accomplished in a streamlined order. Numerous companies planning to remodel their testing approach have placed their trust in AIOps to receive useful insights. According to recent research updates, many large enterprises have exclusively preferred AIOps and sophisticated monitoring tools to examine applications and associated infrastructure. Such operations are a great benefit to the organization as it helps enhance overall business value.
The global AIOps market is estimated to reach USD 237 billion by 2025. These statistics highlight impressive growth that is majorly supported by modern technologies such as machine learning, big data platform, and predictive analytics.
How can Testers Contribute to AIOps Operations?
For the implementation of AIOps, you need to gather a team of testing professionals having a clear idea about the components and steps involved. Similar to DevOps, AIOps also requires a significant cultural shift with the tester placed in an important position.
At the beginning of AIOps implementation, testers’ team needs to get familiar with the AI and ML terminologies. This step is essential and proves beneficial while imparting training. To understand the complete role of a professional tester in an AIOps environment, let us stress on the pre- and post-implementation stages for better insight.
Pre-Implementation Stage of AIOps
AIOps is known to assist teams in smartly carrying out performance testing operations. Usually, performance testing is conducted during the staging environment but without the involvement of production-related data.
Before AIOps is actually implemented, the testing team ensures the scripting of performance testing scenarios. These are then reviewed in collaboration with business and development teams.
The latter approach by the testing team is to execute the load tests. According to professionals’ tests, it was revealed that the scripting failed while the server collapsed when it reached peak memory usage. At this moment, the utilization of system resources was reasonably high.
While the testers analyze the script execution results and application log files, they can ask the IT support team for additional assistance. This includes a comprehensive analysis by the IT team focused on various alerts related to the APM monitoring system.
Post-Implementation Stage of AIOps
During this phase, the testers select performance testing scenarios by seeking help from the IT & infrastructure teams. This stage acts useful towards better decision-making carried out by the business & development teams.
After implementing AIOps, the IT support team gets to prepare curative automated scripts for handling the peak usage of various system resources. At the time of performance test execution, the testers are notified regarding dynamic errors, which are carefully resolved by the IT team’s scripts.
Hence, system resources are very well managed; therefore, the testing team can completely dedicate themselves to performance testing activities. It is essential to understand that even after implementing AIOps, the testers’ capabilities are utilized from time to time per system resources.