5 Ways In Which AI Can Transform DevOps
The data generated in DevOps runs very well into exabytes. Not only it becomes difficult for the DevOps team to effectively absorb the data but also makes it challenging for them to apply solutions from this massive amount of data. The data generated by continuous integration and tools deployment is humongous. In fact, simple issues such as finding critical events usually take hundreds of hours. The number of integrations, the success rate, and defects per integration is only useful when they are timely evaluated and correlated.
Given such a situation, there have been increased efforts to integrate AI into DevOps, which helps save time and increase efficiency. It is expected that in the near future AI will be able to emerge as a tool to compute, analyse and transform how teams develop and manage applications. Apart from its conventional use in the DevOps environment, AI can also prove to be beneficial in addressing security issues and data leaks, for organising memory management, and in garbage collection.
Here are five ways in which AI can transform DevOps:
DevOps includes different types of testing, such as regression testing, functional testing, and user-acceptance testing. These tests generate a large amount of data. AI, in such a situation, can prove to smoothen the system to a great extent. It identifies patterns of such collected data and then evaluates the coding practice that led to an error. Such systems eliminate test coverage overlaps, optimise current testing mechanisms and accelerate from just defect detection to defect prevention. It can also identify module interdependencies, thereby improving the overall product quality.
Managing Project Requirements
Several organisations have introduced AI-enabled digital assistants that can analyse documents, identify inconsistencies, and recommend solutions. Powered by natural language processing, such systems are trained on referenced guidelines to learn high-quality writing requirements. If done manually, the whole process of requirement management, which includes gathering, validating, tracking user requirements, would cause grave delays and in worse cases, complete failure of the final project. With automation in place, the requirements review is more streamlined. In fact, as per a report, such tools helped in reducing the requirement review time by over 50%.
Security is one of the most critical aspects of the DevOps system. Businesses need to protect themselves from security breaches and distributed denial of service (DDoS) attacks. AI and machine learning can be used to identify the abnormal condition and take corrective actions accordingly. Further, AI can be used to augment DevSecOps and improve security by recording threats and deploying an ML-based central logging architecture for anomaly detection.
In Application Development & Enhancement
AI can also be used to help developers during the application process. AI-systems can examine build-compile success, testing completions, and operational performance of the past applications to make recommendations to the developers on the code they are writing. Further, monitoring tools using machine learning can gather information about log files, performance metric, and datasheets to identify issues in advance and make appropriate alterations to the application.
DevOps encourages ‘fail, but fail fast’; considering this, the system needs to be quick in identifying the flaw and sound alert. However, with conventional systems, all incoming alerts are marked with the same severity, making it difficult for the team to react. AI learns several factors such as the magnitude of the alert, past behaviour, and the source of the alert. This way, the team is assisted in prioritising their responses to the alerts without overwhelming the system.
AI in DevOps helps in directing human intelligence and creativity towards more intricate projects and tasks, while it helps with managing the efficiency, speed and productivity of the task, which in turn enhances the customer experience. AI is delivered value to DevOps by auto-suggesting code segments, improving quality assurance techniques, and streamlining requirement management.
The downside of deploying AI-based DevOps is with open-source projects, where the developers may unintentionally propagate security risks into their codes. It is important that AI is introduced in a controlled manner to ensure that they become the backbone of the DevOps system and not act as rogue elements that need constant remediation.