Elements of AI-based Application Release Automation.

 Application Release Automation exists in some form or the other in every Modern enterprise. In the last blog, we discussed the idea of an AIOps-driven Application Release Automation that gives more value to the DevOps team and minimizes downtime. Here, we explore some of the elements of Application Release Automation and explore how AI can enhance each of them.
 1. Code Testing
The first step, of course, is to ensure that the code meets the security and policy standards of the enterprise. Most coding standard tools are based on pre-built libraries and perform rule-based checks. AI-based tools can go a step further and fill in comments, suggest the right changes, and flag issues without relying on static libraries. This is all the more effective with VAPT tools which are based on known attacks. More hackers are adopting AI into their attacks, making AI-based approaches to Vulnerability Testing essential at this stage.
2. Release Scheduling
Release Managers spend a lot of time going over the project work, changelogs, and planning for a release that causes the least disruption. However, machine learning models can simplify this by predicting the right time for releases given technical constraints and work progress rates. It can also take in business considerations like impact on revenue to find the optimal release periods. 
 3. Dependency and Environment Management
This is where GitOps truly comes into play. Having a guaranteed configuration and declared requirements for applications enshrined in Git means that GitOps can handle dependencies and environments quickly and efficiently on any platform. The use of AIOps driven agents also enables automated course corrections in case of failures in creating environments. 
 4. Integrated Testing
Traditional automated testing covers most of the planned test cases and checks for failure scenarios in them. However, it does not cover edge cases and unique errors that are found client-side. Deep Reinforcement Learning based testing can explore an entire series of action spaces by target breaking the software functionality as a reward mechanism. This lets developers find unique issues that would otherwise get completely missed out.
 5. Staging and Shadow Testing
Staging is the step before production - where the environment is as close to production as possible. Typically the service also shadows live traffic and is tested for performance. AI models that are designed to find issues in production environments (e.g. incident recognition) can be reused here for root cause detection. With this approach, developers can cut down on the likelihood of post-launch defects considerably.
 6. Dashboard Analytics
KPI, Log, and Trace Analytics are often used by DevOps teams in production environments. However, they show an equal amount of promise in the staging environment. Using AI-enabled analytics on shadow traffic enables DevOps teams to find anomalies in logs, unusual KPIs, and new trace statistics. AI models can contrast these against old patterns to find and isolate changes that were the most impacted as a result of the new code. This analysis gives a higher level of insight into the impact of code changes and lets DevOps teams adequately prepare for deployment.
 7. Release
Release to production servers is typically the most anxiety-inducing phase for DevOps teams. Any missed bugs or poor configurations can lead to downtime which frequently has a direct impact on revenue or other business KPIs. With AIOps-driven Release Management, agents on production servers can monitor state changes in a system and respond quickly with the correct actions to stabilize the state changes. This lets the DevOps team evaluate the situation without being stuck in firefighting mode. In addition, they are backed up by Anomaly Detection and Incident Recognition models that can quickly identify anomalous patterns and nail down the root cause of the issue. This lets teams decide on the next steps like rollback or hotfixes. In a more advanced AI implementation, Deep Reinforcement Learning-based models can take over this aspect as well by attempting fixes in a controlled action space. If none of the fixes yield in stabilization, it can also roll back the update.
 8. Defect Analysis
AI-based tools can help with defect analysis and post-mortem of an issue by correlating traces, KPIs, and logs to zone down on potential clues, similar to how a developer would approach this problem. This lets DevOps teams cut down on the meantime to diagnose errors and get the new updates running again.
 AIOps has huge potential in some of the key elements of Application Release Automation. Realizing this potential requires enterprises to understand which stage of their release process is the biggest bottleneck before automating it with AI. To learn more about how to approach Automation of Application Release, please visit