CommunityDevOps ExchangePartners
Articles
6/24/2022
10 minutes

AI And DevOps: Partners In Efficiency And Innovation

Written by
Team Copado
Table of contents

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.

How AI and DevOps Can Work Together for More Efficient Development

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. 

Requirements Management

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 Poor or outdated documentation can lead to miscommunication, scope creep, missed deadlines, and other issues.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.

Vulnerability Detection

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.

Security Triaging

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.

QA Test Case Creation and Management

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. 

  1. AI analysis can help spot opportunities to reuse existing test cases even across highly complex development projects and testing suites, so engineers don’t waste time developing redundant tests. 
  2. AI test automation tools can self-heal outdated or broken test cases, reducing the amount of time spent manually updating tests for newer iterations of software. 
  3. AI is often better at detecting defects than traditional programmatic testing and can sometimes even fix bugs without any human intervention.

Monitoring and Incident Response in Production

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: Partners in Efficiency and Innovation

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.

 

Book a demo

About The Author

#1 DevOps Platform for Salesforce

We build unstoppable teams by equipping DevOps professionals with the platform, tools and training they need to make release days obsolete. Work smarter, not longer.

How to Sync Salesforce Environments with Back Promotions
Copado and Wipro Team Up to Transform Salesforce DevOps
DevOps Needs for Operations in China: Salesforce on Alibaba Cloud
What is Salesforce Deployment Automation? How to Use Salesforce Automation Tools
Maximizing Copado's Cooperation with Essential Salesforce Instruments
Future Trends in Salesforce DevOps: What Architects Need to Know
From Chaos to Clarity: Managing Salesforce Environment Merges and Consolidations
Enhancing Customer Service with CopadoGPT Technology
What is Efficient Low Code Deployment?
Copado Launches Test Copilot to Deliver AI-powered Rapid Test Creation
Cloud-Native Testing Automation: A Comprehensive Guide
A Guide to Effective Change Management in Salesforce for DevOps Teams
Building a Scalable Governance Framework for Sustainable Value
Copado Launches Copado Explorer to Simplify and Streamline Testing on Salesforce
Exploring Top Cloud Automation Testing Tools
Master Salesforce DevOps with Copado Robotic Testing
Exploratory Testing vs. Automated Testing: Finding the Right Balance
A Guide to Salesforce Source Control
A Guide to DevOps Branching Strategies
Family Time vs. Mobile App Release Days: Can Test Automation Help Us Have Both?
How to Resolve Salesforce Merge Conflicts: A Guide
Copado Expands Beta Access to CopadoGPT for All Customers, Revolutionizing SaaS DevOps with AI
Is Mobile Test Automation Unnecessarily Hard? A Guide to Simplify Mobile Test Automation
From Silos to Streamlined Development: Tarun’s Tale of DevOps Success
Simplified Scaling: 10 Ways to Grow Your Salesforce Development Practice
What is Salesforce Incident Management?
What Is Automated Salesforce Testing? Choosing the Right Automation Tool for Salesforce
Copado Appoints Seasoned Sales Executive Bob Grewal to Chief Revenue Officer
Business Benefits of DevOps: A Guide
Copado Brings Generative AI to Its DevOps Platform to Improve Software Development for Enterprise SaaS
Celebrating 10 Years of Copado: A Decade of DevOps Evolution and Growth
Copado Celebrates 10 Years of DevOps for Enterprise SaaS Solutions
5 Reasons Why Copado = Less Divorces for Developers
What is DevOps? Build a Successful DevOps Ecosystem with Copado’s Best Practices
Scaling App Development While Meeting Security Standards
5 Data Deploy Features You Don’t Want to Miss
Top 5 Reasons I Choose Copado for Salesforce Development
How to Elevate Customer Experiences with Automated Testing
Getting Started With Value Stream Maps
Copado and nCino Partner to Provide Proven DevOps Tools for Financial Institutions
Unlocking Success with Copado: Mission-Critical Tools for Developers
How Automated Testing Enables DevOps Efficiency
How to Keep Salesforce Sandboxes in Sync
How to Switch from Manual to Automated Testing with Robotic Testing
Best Practices to Prevent Merge Conflicts with Copado 1 Platform
Software Bugs: The Three Causes of Programming Errors
How Does Copado Solve Release Readiness Roadblocks?
Why I Choose Copado Robotic Testing for my Test Automation
How to schedule a Function and Job Template in DevOps: A Step-by-Step Guide
Delivering Quality nCino Experiences with Automated Deployments and Testing
Best Practices Matter for Accelerated Salesforce Release Management
Maximize Your Code Quality, Security and performance with Copado Salesforce Code Analyzer
Upgrade Your Test Automation Game: The Benefits of Switching from Selenium to a More Advanced Platform
Three Takeaways From Copa Community Day
Cloud Native Applications: 5 Characteristics to Look for in the Right Tools
Using Salesforce nCino Architecture for Best Testing Results
How To Develop A Salesforce Testing Strategy For Your Enterprise
What Is Multi Cloud: Key Use Cases and Benefits for Enterprise Settings
5 Steps to Building a Salesforce Center of Excellence for Government Agencies
Salesforce UI testing: Benefits to Staying on Top of Updates
Benefits of UI Test Automation and Why You Should Care
Types of Salesforce Testing and When To Use Them
Copado + DataColada: Enabling CI/CD for Developers Across APAC
What is Salesforce API Testing and It Why Should Be Automated
Machine Learning Models: Adapting Data Patterns With Copado For AI Test Automation
Automated Testing Benefits: The Case For As Little Manual Testing As Possible
Beyond Selenium: Low Code Testing To Maximize Speed and Quality
UI Testing Best Practices: From Implementation to Automation
How Agile Test Automation Helps You Develop Better and Faster
Salesforce Test Cases: Knowing When to Test
DevOps Quality Assurance: Major Pitfalls and Challenges
11 Characteristics of Advanced Persistent Threats (APTs) That Set Them Apart
7 Key Compliance Regulations Relating to Data Storage
7 Ways Digital Transformation Consulting Revolutionizes Your Business
6 Top Cloud Security Trends
API Management Best Practices
Applying a Zero Trust Infrastructure in Kubernetes
Building a Data Pipeline Architecture Based on Best Practices Brings the Biggest Rewards
CI/CD Methodology vs. CI/CD Mentality: How to Meet Your Workflow Goals
DevOps to DevSecOps: How to Build Security into the Development Lifecycle
DevSecOps vs Agile: It’s Not Either/Or
How to Create a Digital Transformation Roadmap to Success
Infrastructure As Code: Overcome the Barriers to Effective Network Automation
Leveraging Compliance Automation Tools to Mitigate Risk
Moving Forward with These CI/CD Best Practices
Top 3 Data Compliance Challenges of Tomorrow and the Solutions You Need Today