DevOps is designed to accelerate the software development process and deliver value to customers faster without compromising code quality. Traditional DevOps has gone through a long development cycle over the past decade. Today, organizations are starting to build CI/CD pipelines following DevOps principles. But in most cases, teams are still relying on a combination of manual processes and people-driven automated processes. Obviously, this degree of optimization does not reach the limit in the ideal or theoretical sense.
Recently, the theoretical high ground of DevOps has ushered in two technical forces of AI and ML. Related tools are starting to converge and radiate tremendous power within the traditional DevOps tool stack. From decision-making process improvement to enhanced automation of operations and code quality, DevOps has a brighter future under the dual blessing of AI (artificial intelligence) and ML (machine learning). Below, we discuss some of the highlights in detail:
First, automated code reviews. In the early stages of software development, starting with the coding itself, AI and ML tools have been able to perform automated code reviews and code analysis based on expressive management-guided datasets (that is, providing input data to machine learning algorithms based on desired machine behaviors and responses). All of this will greatly reduce the workload of humans in code quality management.
Additionally, with code management and collaboration tools, users can automatically distribute review workloads among team members. This process enables earlier detection of code flaws, security issues, and code quality issues by such algorithms. These tools also reduce noise in code reviews. In addition to detecting defects, automated code reviews are responsible for enforcing coding and security standards.
Second, automatic code analysis tools. Intelligent tools powered by AI and ML, such as code analysis and improvement tools, can learn from reports containing millions of lines of code, grasp the intent expressed by the code and document changes made by developers. Based on this, these smart tools can make recommendations for each line of code through analysis.
Other scenarios analyze the code from a different angle. After analyzing millions of pieces of code from open source projects, machine learning tools are able to provide effective performance tuning conclusions, including finding the most expensive lines of code to run, and preventing them from hurting application response times. These tools can find problems in your code, such as resource leaks, potential concurrency contention, and wasted CPU cycles. What’s more, these tools can be smoothly integrated with CI/CD pipelines during the code review phase and application performance monitoring phase.
After coding the new functionality, developers can then study how to develop automated unit tests powered by AI and ML. In the development sprint phase, this intelligent unit testing tool can help developers save about 20% of their time.
Third, self-healing tests. After completing the build and integration work, the next stage is to implement functional and non-functional testing. At this stage, using AI and ML to create code and perform self-healing testing/maintenance has become a reality in the DevOps space.
Of course, test automation itself may also become a huge bottleneck, and it is also the reason why many projects are frequently delayed. The testing process is slowed down by unreliable, unstable automated processes that often result from rapid changes in the application under test and various elements of the testing process. The advent of smart technologies can help identify these changes and adjust test methods immediately, thereby achieving stability and reliability in the test process.
Fourth, low-code/no-code tools. For mobile and web applications, in the past, we needed to invest huge resources to cultivate relevant talents to acquire stable and reliable code testing skills. In this regard, AI and ML testing tools can comprehensively learn the application flow, screen content, and elements, and ultimately generate tests automatically in a low-code or even no-code manner. These tools can also improve themselves during each round of testing to enhance test quality.
Low-code or no-code tools allow team members to participate in the development of automated tests. Once done, developers will save a lot of time and focus on other, more pressing tasks—such as developing other innovative features.
Fifth is robotic process automation. RPA (Robotic Process Automation) takes automated testing to a new level using AI and ML. Such technologies can automate numerous processes in large organizations that were previously manual, time-consuming, error-prone, and difficult to automate.
Sixth, test impact analysis tools. Once the tests are executed, AI and ML test impact analysis (TIA) tools can take over to guide decision makers on which tests need to be continued in subsequent releases and which tests can be eliminated in the subsequent process. In addition, under the same test category, AI and ML algorithms can determine the root cause of failures based on guided test data, thereby significantly reducing mean time to resolution (MTTR).
Seventh, AIOps. Later in the DevOps process, before and after deploying code to production, AI and ML are emerging technologies leading the way in AIOps. A complete AIOps solution not only covers intelligent APM (Application Performance Monitoring) but also introduces ITIM (IT Infrastructure Monitoring) and ITSM (IT Service Monitoring) mechanisms. Together, they form an integrated layer of production and operational insights that can run on top of big data and work against advanced modern software architectures (microservices, cloud architectures, etc.).
With AI-based operational capabilities, teams can focus on determining the service health of applications, while keeping full control and visibility of production data. Building on this, DevOps is able to further reduce MTTR using real-time automated incident management. Among them, AI and ML will be responsible for providing log observability, trend aggregation, and corresponding prediction results for production-level applications.
Using these tools in an AIOps portfolio, teams can reduce and prevent service outages (predictive alerts), speed up troubleshooting, and quickly analyze large log files to identify root causes and categories such as security, network, server, etc.
Summarize
The quest for DevOps and human engineering will never go away, but in the quest, we can use current results to optimize and speed up error-prone areas that were previously difficult to automate or maintain.
AI and ML are great solutions to these challenges, and decision-makers can derive great value from such tools by analyzing the problems. Of course, the expected value can only be achieved by seamlessly integrating these solutions with legacy processes and tools. If AI and ML cannot be easily integrated into the standard DevOps tool stack, the project will become mere rhetoric and eventually regress back to traditional software development practices.
Author Bio
Santhosh is a Digital Marketing Executive at Sparkout tech solutions. He designs marketing strategies with the intention of using high-quality content to educate and engage audiences. His specialties include social media marketing specialist and SEO, and he works closely with B2B and B2C businesses, providing digital marketing strategies that gain social media attention and increase your search engine visibility
