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Automation is key to DevOps maturity because it streamlines tedious and repeatable tasks in development and testing while reducing bottlenecks in the software development lifecycle (SDLC). Although most DevOps automation tools use programmatic approaches to automation (such as imperative or declarative programming), the advancement of artificial intelligence (AI) technology is creating new opportunities to use automation more effectively.
Overall, AI has not seen widespread adoption in the DevOps field, due in part to the technology’s complexity and relative newness. However, DevOps organizations face many challenges that could be solved with the integration of AI tools. In this article, we’ll cover some of the ways that you can use AI and DevOps together to drive efficiency, innovation, and business success.
Introducing AI into your DevOps SDLC doesn’t mean embracing unpredictable AI pipelines. However, there are tasks where AI and neural networks can supplement developers’ work.
Every well-oiled SDLC is based on robust planning and comprehensive documentation. Traditionally, requirements were managed using spreadsheets, emails, documents, wikis, and other manual tools. However, manually managing requirements for highly complex modern software projects can create a logistical nightmare. Poor or outdated documentation can lead to miscommunication, scope creep, missed deadlines, and other issues.
To address this challenge, AI-powered requirements management tools leverage Natural Language Processing (NLP) technology to analyze user stories and requirements. These tools can flag potential issues or conflicts, suggest fixes and improvements, and even estimate realistic schedules for completing project tasks. Using an AI requirements management solution reduces the amount of time spent manually reviewing, updating, and sharing requirements. This ensures that no mistakes or issues slip through the cracks.
Another challenge many DevOps organizations face is maintaining the speed and agility of their software pipeline without sacrificing security. Frequent merges and releases, a cornerstone of DevOps practices like CI/CD (continuous integration/continuous delivery), increase the risk that a security vulnerability could be introduced to your codebase. To combat this, you need automated security testing, which typically uses signature-based variant analysis to compare your code to a list of known vulnerabilities.
While this type of security test automation is more effective than manual variant analysis, it still suffers from some limitations. For example, it often fails to recognize new threats if they don’t exactly match a known vulnerability. AI-powered security testing solutions overcome these limitations using artificial neural networks that are better at recognizing patterns that fall outside of known parameters. The AI can even use past experience and existing signature databases to extrapolate what new vulnerabilities may look like, making them more effective at detecting threats.
Even with automated vulnerability detection, security tends to be a bottleneck in DevOps pipelines due to the need for security teams to dig through alerts and prioritize vulnerabilities based on how serious they appear to be. It takes human security engineers time to sort out the false positives from the true, determine the relative severity, and assign issues to the appropriate team members. These delays can affect your ability to release software on time. In addition, slowdowns can create friction between security engineers and developers who should, ideally, work together harmoniously to support common goals.
DevOps security automation tools have started using AI to analyze detected vulnerabilities and better determine which are legitimate threats and which can be safely ignored. These tools assign quantitative threat levels (e.g., 1 for minor and 5 for severe) to help engineers triage vulnerabilities and can even automatically assign security incidents to the experts best suited to resolve them.
Another potential bottleneck in your DevOps pipeline is QA. Testing is required to ensure that software meets requirements and functions as expected, but manually performing these tests (and then waiting for any found issues to be resolved and then retested) can significantly delay the fast-paced schedule of DevOps releases. Test automation begins to solve that problem, but most test automation tools still require engineers to create and update test cases for each iteration of code.
AI-powered test automation helps DevOps organizations overcome this challenge in a few interesting ways.
A DevOps lifecycle doesn’t end when the software is released. You must also monitor your application in production and collect feedback from customers so you can fix any issues as quickly as possible. Shortening this feedback loop is critical to DevOps success because it allows you to constantly improve the quality of your end product without slowing down your release cycles.
Some DevOps monitoring tools use AI to analyze the performance of software and recommend ways to optimize in future releases. When issues are detected, AI can perform root-cause analysis to determine why the problem occurred, recommend ways to fix it, and even resolve minor or common issues without human intervention. For those problems that require an engineer’s expertise, the AI can automatically generate incident reports and assign them to the appropriate team members.
AI and DevOps work well together because artificial intelligence can improve your automation capabilities and eliminate human bottlenecks in your pipeline. DevOps industry leaders are finding new ways to incorporate AI into the SDLC, using technology like natural language processing, artificial neural networks, and self-healing test automation to drive innovation and increase efficiency.
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