The need for speed in the IT world is increasing by the day. Software development that took months earlier is being executed in weeks by collaborating distributed teams using DevOps methodologies.
Since the term ‘DevOps’ entered our digital lexicon, IT organizations worldwide have leaped to this methodology for good reasons. DevOps dramatically improved software development efficiency, speed, and quality by breaking down the traditional barrier between development and IT operations teams.
Now for the B-side: Despite gaining universal acknowledgment, the road to DevOps adoption has remained bumpy. Why? Many challenges—from skill sparsity to a multifarious toolset—plague the DevOps methodology.
The ultimate goal of DevOps—100% automation across the software development lifecycle (SDLC)—remains elusive. Moreover, the patchy adoption of DevOps has created a two-paced digital landscape, with some enterprises having witnessed some degree of success in DevOps adoption. In contrast, others struggle with integrating DevOps into overall business processes.
Artificial Intelligence (AI) is an elixir to these challenges. AI can remarkably impact DevOps, such as reducing the operational complexities due to the toolsets’ high niche and distributed nature.
By streamlining and accelerating every phase of the software development lifecycle, AI takes DevOps to a new level of accuracy, quality, and reliability. Without further ado, let’s dwell upon the ten ways AI is transforming DevOps methodology:
1) Testing
DevOps encompasses a host of testing processes, including unit testing, regression testing, functional testing, and user acceptance testing. Typically, these processes generate massive amounts of data, making it challenging for DevOps teams to analyze data.
Artificial intelligence is invaluable in analyzing and categorizing large amounts of data based on patterns. It then provides insights into poor coding practices and errors, which helps coders identify areas for development and improve their performance.
2) Data Access
Lack of adequate access to data is one crucial aspect impeding the productivity of the DevOps team. It hinders the team’s capability to leverage data for decision-making. AI-powered data mapping technologies integrate a myriad of data from siloed sources and streamline it for consistent and repeatable analysis. This will help the technology teams uncover valuable insights and make vital decisions.
3) Real-time Alerts
Prompt alerts promote rapid response. However, a horde of alerts with the same severity level makes it challenging for tech teams to react. AI can help prioritize the most critical issues by collecting all the diagnostic information about them. Based on the magnitude of the alert, past behavior, and the source of the alert, AI can also recommend a prospective solution and help remediate the issue faster.
4) Automation
Automation is AI’s intrinsic value proposition. Integrating AI with DevOps processes significantly improves the automation quotient by eliminating or reducing the need for human intervention across processes from code changes to deployment.
Also Read: AI-Powered, ML-Driven – The New DevOps Trend!
5) Security
As an extension of the DevOps methodology, DevSecOps ingrains security into the DevOps workflow and automates the core security tasks across SDLC. AI can augment DevSecOps to deliver higher performance. AI-based anomaly detection techniques can help DevOps teams accurately spot potential system threats and secure them proactively.
6) Collaboration
DevOps promotes collaboration, but the team’s incumbent cultural differences impede optimal collaboration. Culturally, developers aim to release code faster and more frequently, and Ops teams work to ensure minimal disruption to operations.
Again, AI can be a game-changer. It can improve DevOps teams’ collaboration by facilitating a unified view of system issues across the DevOps toolchain. Further, AI also helps DevOps teams gain valuable insights into detected anomalies and the redressal process.
7) Feedback
DevOps teams can go astray when feedback is not timely, dragging the release timeline backward. A continuous feedback loop fosters agility, quality, and speed. Monitoring tools are injected into DevOps phases to collect feedback, but these tools fail to provide end-to-end insights about cycle health. AI can help absorb and uncover insights from data streams from multiple monitoring tools, giving an accurate and complete picture of application health.
8) Faster Redressal
Enterprises go the extra mile regarding software QA processes to prevent bugs, logical issues, and unreliable code sections. Recalling or retrofitting releases is expensive and impacts brand reputation.
AI-based QA tools can help technology teams predict application failure points before delivering them into customer environments. AI can also troubleshoot defects in complex applications and platforms that have already been delivered to customers.
9) Software Quality
AI is proving adept at improving software quality by auto-generating and auto-running test cases on the code. AI-based testing tools eliminate test coverage overlaps, enhance existing testing procedures, and accelerate the process from bug detection to prevention.
10) Requirements Management
AIOps Solutions helps streamline every phase of requirements management – creating, editing, testing, and managing requirements documents. AI-based tools can identify issues ranging from incomplete requirements to escape clauses, thereby improving the accuracy and quality of requirements.
In conclusion
AI has already transformed the way IT organizations do business. Now, it has forayed into the field of DevOps to unleash its full potential by making SDLC more intelligent, improving team velocity, and eliminating human errors. With the infusion of AI, DevOps teams can realize the benefits of self-learning autonomous systems in every phase of the DevOps development cycle.
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