DevOps methodology has significantly improved software development by breaking down the traditional barrier between development & IT teams. This collaboration of distributed teams helps in reducing the timeline of software development. However, the ultimate goal of DevOps – which is 100% automation across Software Development Lifecycle (SDLC) – remains unachieved. Some business organizations still seem to be struggling with how to integrate DevOps in overall business processes.
These challenges can be maneuvered by adopting AI. The highly distributed nature of AI toolsets helps in reducing operational complexities of DevOps methodology. AI also improves the accuracy, quality and reliability of DevOps by streamlining and accelerating different phases of software development.
Ways in which AI transforms DevoPS:
- Testing: DevOps includes a number of testing processes such as unit testing, regression testing, functional testing, and user acceptance testing. These testing processes generate a large amount of data and analyzing this data can be overwhelming for the DevOps team. AI implements pattern recognition to make it easier to analyze and categorize the data. After analyzing, it also provides insights on poor coding practices and errors to help code developers identify areas for better performance.
- Data Access: The productivity and efficiency of DevOps team is highly stalled by lack of adequate access to data. This hinders the team’s ability to leverage data for decision-making. AI-powered data mapping technologies integrate a myriad of data from different sources & streamline it for consistent & repeatable analysis. It helps teams uncover valuable insights for decision-making.
- Real-Time Alerts: Prompt alerts are helpful in promoting rapid response. However, when DevOps teams receive multiple alerts with same level of severity, it becomes difficult for them to react effectively. In such situations, AI helps in prioritizing most critical issues by collecting diagnostic information pertaining to every issue. In addition to prioritizing the issues, AI also suggests prospective solution based on magnitude of alert, past behavior, & source of alert. This facilitates faster remediation of the issue.
- Automation: Integration of AI with DevOps significantly improves the automation quotient by eliminating or reducing the need for human intervention across processes from code changes to deployment.
- Security: DevSecOps is an extension of DevOps that ingrains security into DevOps workflow. It automates core security tasks across software development lifecycle. AI based anomaly detection techniques help teams to accurately spot threats to their system and secure it proactively.
- Collaboration: AI plays an important role in improving collaboration between DevOps teams by facilitating a single, unified view into system issues across DevOps toolchain.
- Software Quality: AI improves the quality of software by auto-generating and auto-running test cases on the code. AI-based testing tools eliminate test coverage overlaps and fasten the process from bug detection to bug prevention.
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