Software delivery is a complicated space and there are gaps when it comes to quality engineering and assurance in development lifecycle. These gaps are more profound as businesses are under increasing pressure to deliver new applications and updates to customers faster than ever, and with zero error margin. Current agile approaches have solved problems to an extent, but there are still processes that are highly complicated, manual and error-prone.
A long-standing remediation to all these potential gaps has been integrating quality engineering and DevOps for Continuous Delivery (CD) in re-engineering applications for accelerating digital transformation. CD is a practice in which code changes are built, tested and automated for release into production to create a continuous feedback loop for faster delivery and quality improvement in business application.
DevOps seeks to unify software development, operations and testing. Organizations underway in their DevOps journey understand that testing often and quicker is the key to successful DevOps implementation but still run into application performance and automation problems.
The Existing Gap in CD
Many companies have implemented CD as part of their DevOps journey, but there are gaps around what metrics to measure and improve around DevOps. Also, there are not defined and industry-adopted statistics, leaving it to the discretion of practitioners in every organization. DevOps is a system or set of practices that require measurement to have a reasonable impact and improvement in business metrics.
AI and ML Can Enhance the CD Pipeline
Organizations implementing DevOps practices to improve application quality and delivery speed need to track key metrics and KPIs that can be used to predict quality and performance issues proactively. This is possible by leveraging artificial intelligence and machine learning technologies to improve different areas of the CD pipeline. An AI-driven quality intelligence and DevOps platform helps businesses working on customer-facing applications achieve faster time to market as well as higher quality, resulting in increased brand value using CD and quality engineering best practices.
Mature DevOps toolchains across organizations are capturing data in various forms (such as git commits, milestones and releases, infrastructure deployments, test execution, build logs, application log files and so on) and leveraging them to build AI and ML algorithms that can improve efficiency. ML has already been used to analyze network data to find anomalies and prevent network hacks; the healthcare industry is using them for personalized medicine by understanding personal genomic structure; and BFSI is using them to identify fraudulent attempts in financial activities.
Similarly, the power of data can be used in real-time application and infrastructure monitoring tools to predict incidents in production environment avoiding any roll-backs or downtimes. Business systems and applications can be improved using advanced algorithms to predict issues and remediate them automatically.
Bottom Line: If You’re Not Using AI, You’re Missing Something
There is a constant need to monitor entire infrastructures and development pipelines, including events and containers, to streamline release cycles, increase the efficiency of DevOps and IT operations, and reduce troubleshooting time in production.
Use AI in your development. DevOps is not only about continuous delivery — it’s also about continuous innovation in terms of making sure that the end user is satisfied. You need tools and accelerators to set up dashboards quickly and easily to monitor the resulting log data of system and user behavior to keep an eye on transactions and behavior with the goal of constantly delivering the best software.
About Prasanna Singaraju
Prasanna Singaraju is the Co-Founder and Chief of Engineering and Technology at Qentelli. A natural leader with a penchant for technology, Prasanna brings diverse experience which ranges from RF Engineering to Infrastructure - Data Center and Unified Communications toQuality Assurance, Product and Project Management. An ardent follower of technology, Prasanna believes in sustainable innovation and value driven solutions and service delivery. At Qentelli, Prasanna heads Engineering Services, Solutions and Service Delivery and the Innovation Group.