Artificial Intelligence has been adopted across almost all industries and its impact in transforming technology is much greater than we can imagine. AI technology enables machines to become smarter, allowing them to execute simple and complex tasks similar to humans and even think like humans. Currently, AI technology limits have not been reached as the technology is finding new applications each day and for a wide variety of purposes.
What is DevOps?
DevOps can be defined as a group of methods that incorporate software development and information technology processes. It enables the development, testing and release of the software. The aim of implementing DevOps is to provide regular feedback at each stage of various processes. DevOps closes the gap between the development and operations teams.
How AI Can Transform the DevOps
In the future, DevOps will certainly be AI-driven. Artificial intelligence will evolve into an essential tool for computing and analyzing, revolutionizing the way teams build, deliver, deploy and manage applications, as humans are unable to manage and process massive quantities of data and computing for daily operations.
DevOps and AI are intricately linked to each other as DevOps is a business-driven methodology towards software delivery, and AI technology can be integrated into the system to enhance its functionality. DevOps teams are capable of testing, coding, releasing and monitoring software more efficiently by using AI. Ai is also crucial for automation, capable of identifying and resolving issues quickly and increasing team cooperation.
Outlined below are a few ways that indicate how AI can take DevOps to the next level:
Adds to the Efficiency of Software Testing
DevOps benefits from the incorporation of AI as the technology improves the overall software development process and streamlines testing. Functional testing, regression testing and user acceptance testing create massive quantities of data. AI-based test automation tools can detect inferior coding habits that are responsible for frequent errors by deciphering the pattern within the data obtained by delivering the output. This type of data can then be utilized to further boost productivity.
Makes Root Analysis Faster
To locate the root cause of a failure, AI searches for patterns between cause and behavior. Engineers are often too busy with making a project live to analyze failures thoroughly. They study and resolve issues by taking a surface-level approach, avoiding carrying out a deep and thorough root cause analysis. Quite often this fixes the issue enough to make things work but the core cause remains unexplained and can lead to more complicated problems in the future. Hence, carrying out a root cause analysis is crucial for permanently resolving a problem. Artificial intelligence plays a key role in this scenario.
Higher Levels of Security
Currently, DDoS (Distributed Denial of Service) is a popular method of attacking networks and systems. It can affect any organization or website, large or small. Machine learning and artificial intelligence can help in identifying and managing these hazards. An algorithm can be utilized to distinguish between abnormal and normal conditions and then take appropriate actions. For improving overall security, DevSecOps can be improved with artificial intelligence. To detect anomalies and threats, it incorporates a centralized logging architecture.
Enhanced Levels of Traceability
AI enables DevOps teams to communicate with each other more effectively, especially over long distances. AI-driven insights are helping businesses comprehend how shared criteria and specifications can be used to represent localization, unique needs of the client, and create customized performance benchmarks.
Streamlining Requirements Management
DevOps teams can leverage AI and machine learning-based management tools to save time allowing them to focus on developing and creating software products, which they are expected to finish on a regular basis under tight deadlines. Receiving the correct specifications of the project at the start helps to keep the project on track and achieve its crucial milestones.
AI-driven software development tool providers are quickly developing and releasing new applications in their field as they have recognized the need to enable businesses to keep projects on schedule. The quick adoption of Natural Language Processing techniques in this field of DevOps technologies is innovative and exciting to see.
Conclusion
By predicting the needs of developers, AI is assisting in the acceleration of all phases of DevOps development cycles. The implementation of AI in DevOps has added value to three key areas: auto-suggesting code segments, increasing software quality assurance methodologies using automated testing and automating requirements management.
