Since the last decade, the term DevOps entered our collective lexicon, and technology teams all over the world have started to adopt this methodology. Devops breaks down the traditional barrier between development and IT operations teams. There are various advantages for enterprises adopting a DevOps lead approach to deal with how they build software. One of the most important is that- it speeds up the time-to-market for software releases through increased deployment frequency. Another one is, bug fixes can be delivered rapidly and with less hassle because of automated tool chains. And the next one is, mechanization that devops brings dramatically improves the availability to concentrate on innovation, instead of being caught in the ordinary pattern of bug fixes and routine fire-fighting.
There is more complexity in managing and monitoring the DevOps environment. It gets difficult for the DevOps team to manage the magnitude of data in today’s dynamic and distributed application environment. The team needs to manage data that can be in Exabyte. So, it becomes challenging for a human to deal with huge data and make operations to solve customer’s issues. It requires more time to deal with that data. A human can’t analyze the whole data manually.
Know the need of devops in mobile app development at- DevOps in mobile app development- Why and How?
In such a situation, there have been increased efforts to integrate AI and DevOps, that helps to save time and increased efficiency. Expecting that, in the near future AI will emerge as a tool to compute, analyse and transform how teams manage and develop applications. Aside from its regular use in the DevOps environment, AI can also prove to be helpful in addressing security issues and data leaks, for organizing memory management, and in garbage collection. Here we came with some important ways in which AI can transform DevOps.
1. Automated Code Reviews And Code Analysis Tools-
In the early phases of software development, from coding itself, AI and ML tools can perform automated code reviews and code analysis based on data sets (the inputs to an ML algorithm, based on which the machine acts and responds). This reduces human involvement. With the use of code management and collaboration tools, users can automatically distribute the workload of reviews among team members. The outcome is prior detection of code errors, security issues, and code-related defects that such algorithms can spot seamlessly. Such tools also provide noise reduction within code reviews. Apart from detecting defects, automated code reviews also implement coding and security standards. Tools powered by AI and ML, like code analysis and development can learn from repositories filled with millions of code lines.
Such tools can understand the goal of code and note the changes developers are making. These tools can give suggestions to each line of code that they analyze. Some others analyzes the code in a different way. After analyzing a huge code from open source projects, code performance powered by machine learning tools focuses on performance and detects the code that affects application’s response time. These tools can detect issues like resource leaks, potential concurrency race conditions, wasted CPU cycles and can be integrated with CI/CD pipeline in code review stage and app performance monitoring stage.
2. Better Data Correlation Across Platforms-
In an advanced ecosystem, teams use many development and deployment environments. Each environment runs into its own set of issues and errors that are detected by monitoring tools. Without a strong structure for communication, there will be minimal mutual learning across this teams, implying that most of them experience siloed learning cycles. Getting all the issue data into single data lake and applying AI can improve data correlation from various platforms and so it accelerates the learning cycle. Consider an example of monitoring tools in which ML can be applied to get insights from data streams of multiple monitoring tools.
3. Low-Code/No-Code Tools-
Creating a robust test code for mobile and web apps is mostly expensive. AI and ML testing tools generate tests automatically with little to no code by learning the app flows, screens and elements. Tools can self-heal between each test run. No code or low code tools lets your team members to participate in test automation creation activities. Also, it reduces the time required to focus on crucial activities like creating innovative new features.
4. Software Testing-
Artificial intelligence helps in improving process development and testing of development. Devops uses multiple types of testing like regression testing, user acceptance testing, functional testing and huge data is produced from these testing. Artificial intelligence identifies the pattern of collected data and then recognize coding practices that prompted the error. So DevOps team can use this information to improve efficiency.
5. Timely Alerts-
DevOps teams need a developed alert system to spot errors rapidly. Now and again alarms come in gigantic numbers, and all are set apart with a similar seriousness. Sometimes, alerts come in large numbers and all are marked with similar severity and this makes it difficult for developers to react. ML and AI both can help teams to prioritize their responses depending on factors like previous behavior, alert intensity and its source. They can effectively manage such situations when systems are filled with data.