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How artificial intelligence (AI) and machine learning are changing DevOps
Artificial intelligence and machine learning bring new automation power to DevOps. Let's examine seven examples of how these technologies can optimize operations
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.
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AI and ML meet DevOps: 7 trends to watch
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:
1. Automated code reviews
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.
[ Do you understand the main types of AI? Read also: 5 artificial intelligence (AI) types, defined. ]
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.
2. Automated code analysis tools
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.
3. Self-healing tests
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. Among the root causes for non-trustworthy test automation are things like constant changes to the apps under test and to the elements that are being used within the tests. Smart technologies can help identify these changes and adjust tests to make them more stable and reliable.
4. Low-code/no-code tools
Additionally, skills to create robust test code are expensive and not always available, especially for digital apps like mobile and web. Here, AI and ML testing tools can generate tests automatically with little to no code at all by learning the app flows, screens, and elements. The tools can self-heal between each test run.
Low-code or no-code tools allow more of your team members to participate in test automation creation activities. They also free up developers’ time to focus on more pressing activities, such as creating innovative new features.
5. Robotic process automation
An additional layer of test automation using AI and ML that is emerging is RPA (robotic process automation). Such technology can be utilized to automate a lot of manual, time-consuming, error-prone, and hard-to-automate processes within large organizations.
6. Test impact analysis tools
Upon test execution completion, AI and ML test impact analysis (TIA) tools are well-positioned to guide decision-makers on which tests should go on to the next build, which areas are not covered, and more. Under the same category of testing, AI and ML algorithms can identify root cause analysis of failures based on thought test data and save a great amount of mean time to resolution (MTTR).
Later in the DevOps process, prior to and post code deployment to production, AI and ML are leading the emerging technology within AIOps. An AIOps complete solution not only covers smart APM (application performance monitoring), but also leverages ITIM (IT infrastructure monitoring) and ITSM (IT service monitoring). Together, these build a comprehensive layer of production and operational insights analysis that can run on big data and against advanced modern software architecture (microservices, cloud, etc.).
With the power of AI-based operations, teams can focus on determining the service health of their applications and gain control and visibility over their production data. With that, DevOps teams can expedite their MTTR using automated incident management quickly and in real-time. Here, AI and ML are able to do even more in terms of logging observability, trends, and predictions within apps in production and more.
Using such tools within the AIOps portfolio, teams can reduce and often prevent service downtime (predictive alerting). They can also expedite support ticket resolution, analyze large log files more quickly, and identify root causes and categories (security, network, servers, etc.).
The bottom line
While DevOps and human engineering will never go away, they can surely use some help to optimize and expedite tedious, error-prone activities that are hard to automate and maintain.
AI and ML are a great solution to these challenges, and with a proper analysis of issues per each organization, decision-makers can gain great value from such tools. The success will be seen only in cases of seamless integration of such solutions to existing processes and tools. If AI and ML cannot easily integrate into the standard DevOps tool stack, projects will not realize value and eventually revert back to the traditional software development practices.
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