[IN PROGRESS]

Purpose

‘Deployment Success Rate to High-Level environments’ metric is intended for Azure DevOps Pipelines (ADOP) data source and measures the effectiveness of the deployment processes. The result expressed as a percentage, where a higher percentage indicates a higher rate of success. This metric provides insight into the stability, reliability, and efficiency of the deployment mechanisms, which are crucial for continuous delivery and integration practices.

Primary Dimension: Productivity
Secondary Dimensions: -

How metric helps

The metric offers insights into process quality, operational efficiency, and organizational alignment with DevOps practices. Regardless of whether the metric is too high or too low, taking proactive steps to address the underlying causes will help optimize your deployment process, improve team productivity, and deliver consistent value to end-users and stakeholders.

Why it is valuable:

Reduces Downtime: Reliable deployments reduce the risk of downtime in production environments, ensuring business continuity.
Accelerates Delivery Cycles: Teams that avoid repeated deployment failures spend less time troubleshooting and more time innovating, accelerating overall delivery timelines.
Supports DevOps Goals: It reflects a team's ability to implement continuous delivery practices effectively, reinforcing DevOps principles.
Customer Satisfaction: A healthy success rate minimizes disruptions and ensures a seamless experience for end-users.

Condition	Potential risks	What to do

Condition	Potential risks	What to do
The metric is 100%	If the rate is consistently perfect, it could suggest the team is overly conservative, avoiding changes or experimenting with complex features that could lead to temporary issues. This may stifle innovation and growth. It might point to overly lenient quality checks or inadequate pre-deployment testing. In this scenario, failures may occur in production rather than getting caught earlier in lower environments.	Introduce more comprehensive testing frameworks in CI/CD pipelines for edge cases and stress scenarios. Encourage experimentation and risk-taking by creating safe deployment strategies such as canary releases or feature flags. Review the scope of validation to ensure deployments are being rigorously tested for potential challenges.
The metric is too low	Rollbacks or failed deployments may disrupt production environments, degrade user experience, and waste time and effort. Repeated failures in deployments can frustrate teams, erode confidence, and lead to burnout. Downtime or failed deployments in production environments can result in financial losses and reputational damage.	Investigate the reasons behind failures—whether they stem from poor code quality, inadequate test coverage, or insufficient environment readiness. Improve the automation of quality checks, testing frameworks, and approval processes in lower environments before reaching high-level environments. Ensure strong collaboration between development, QA, and operations teams so that deployments are aligned with stakeholder expectations. Conduct simulations or dummy runs of deployments in test environments to detect potential failures in advance.

The ‘Deployment Success Rate to High-Level Environments’ is a critical measure of deployment reliability in Azure DevOps Pipelines.

How metric works

Chart overview

The vertical bar chart shows a number of deployments to high-level environments (axis Y) on a timeline (axis X) selected via the metric parameters (start date, end date) and split by intervals (by week/month/quarter).

The metric is calculated based on the following default values selected in parameters:

StartDate - 01.09.2024
EndDate - 28.02.2025 (not included)
SplitBy - Month

Date format: DD.MM.YYYY

The parameters can be changed via the Configure Parameters option

On hover over a series a hint appears containing:

Timeline (StartDate + EndDate)
Deployment frequency
Number of deployments

Metric thresholds:

Red: -
Amber: -
Green: -

Clicking on a bar the Drill-down appears containing:

Run ID
Stage ID
Number of times
Date

Calculation

Deployment Frequency to High-Level environments is number of successful and unsuccessful pipeline stages runs into high-level environments. The stages are selected in Azure DevOps Pipelines data source configuration in 'Deployment to high-level environment' field (See also PERF Data Source - Azure DevOps Pipelines).

PerfQL

Data Source

Data for the metric can be collected from the Azure DevOps Pipelines data source (Import API type).

TelescopeAI User Guide

[ADOP] Deployment Success Rate to High-Level environments