How artificial intelligence (AI) and machine learning are changing DevOps
DevOps engineering is all about accelerating software development processes to deliver value to customers faster, without compromising code quality.
Traditional DevOps has come a long way over the past decade and now allows many organizations to implement a CI/CD pipeline. However, in most cases, teams are still relying on a combination of manual processes and human-driven automation processes. This is not as optimized as it can or should be.
Recently, the DevOps landscape witnessed the rise of AI and ML technologies. These tools are becoming strong candidates for blending into the traditional DevOps tool stack. From decision-making process improvements to automated operations and code quality enhancements, the future of DevOps looks promising with the help of AI and ML. Here are seven trending changes to watch:
In the early stages of software development, from coding itself, AI and ML tools are already able to perform automated code reviews and code analysis based on thought data sets (the inputs to an ML algorithm, based on which the machine acts and responds.) These serve to reduce human involvement.
Also, with code management and collaboration tools, users can automatically spread the workload of reviews out among members of their teams. The end result is earlier detection of code flaws, security issues, and code-related defects that such algorithms can spot easily. These tools also provide noise reduction within code reviews. In addition to detecting defects, automated code reviews also enforce coding and security standards.
Smart tools powered by AI and ML, such as code analysis and improvements, can learn from repositories filled with millions of lines of code. These tools can then understand the intent of the code and note the changes developers are making. From there, these smart tools can offer suggestions to every line of code they analyze.
Others take a different approach to analyzing code. After analyzing millions of code reviews from open source projects, code performance powered by machine learning tools focuses on performance and helps find the most expensive lines of code that hurt application response time. These tools can find issues in code like resource leaks, potential concurrency race conditions, and wasted CPU cycles, and they can also be integrated with a CI/CD pipeline, both in the code review stage and the application performance monitoring stage.
Under this same category, after coding a new feature, developers can start looking at automated unit test creation driven by AI and ML. This can save around 20 percent of the developers’ time within a sprint.
The next stage of coding post-build acceptance and integration is functional and non-functional testing. Here, code creation using AI and ML and self-healing test code and maintenance are becoming a reality in the DevOps space.
Test automation can be a huge bottleneck and is often the reason that projects are delayed. Flaky automation that cannot be trusted slows the testing process.
