What are the Signs of Declining DORA Metrics?

This blog was originally published in the Typo blog.

Software development is an ever-evolving field that thrives on teamwork, collaboration, and productivity. Many organizations started shifting towards DORA metrics to measure their development processes as these metrics are like the golden standards of software delivery performance.

But here’s the thing: Focusing solely on DORA Metrics isn’t just enough! Teams need to dig deep and uncover the root causes of any pesky issues affecting their metrics.

Enter the notorious world of underlying indicators! These troublesome signs point to deeper problems lurking in the development process that can drag down DORA metrics. Identifying and tackling these underlying issues helps to improve their development processes and, in turn, boost their DORA metrics.

In this blog post, we’ll dive into the uneasy relationship between these indicators and DORA Metrics, and how addressing them can help teams elevate their software delivery performance.

What are DORA Metrics?

Developed by the DevOps Research and Assessment team, DORA Metricsare key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. With its data-driven approach, software teams can evaluate of the impact of operational practices on software delivery performance.

Four Key Metrics

• Deployment Frequency measures how often code is deployed into production per week.
• Lead Time for Changes measures the time it takes for code changes to move from inception to deployment.
• Change Failure Rate measures the code quality released to production during software deployments.
• Mean Time to Recover measures the time to recover a system or service after an incident or failure in production.

In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.

Signs leading to Poor DORA Metrics

Deployment Frequency

Deployment Frequency measures how often a team deploys code to production. Symptoms affecting this metric include:

• High Rework Rate — Frequent modifications to deployed code can delay future deployments as teams focus on fixing issues.
• Oversized Pull Requests — Large pull requests can complicate the review process, causing delays in deployment.
• Manual Deployment Processes — Reliance on manual steps can introduce errors and slow down the release cycle.
• Poor Test Coverage — Insufficient automated testing can lead to hesitancy in deploying changes, impacting frequency.
• Low Team Morale — Frustration from continuous issues can reduce motivation to deploy frequently.
• Lack of Clear Objectives -: Unclear goals leads misalignment and wasted efforts which hinders deployment frequency.
• Inefficient Branching Strategy — A poorly designed branching strategy result in merge conflicts, integration issues, and delays in merging changes into the main branch which further impacts deployment frequency.
• Inadequate Monitoring and Observability — Lack of effective monitoring and observability tools can make it difficult to identify and troubleshoot issues in production.

Lead Time for Changes

Lead Time for Changes measures the time taken from code commit to deployment. Symptoms impacting this metric include:

• High Technical Debt — Accumulated technical debt can complicate code changes, extending lead times.
• Inconsistent Code Review Practices — Variability in review quality can lead to delays in approval and testing.
• High Cognitive Load — Overloaded team members may struggle to focus, leading to slower progress on changes.
• Frequent Context Switching — Team members shifting focus between tasks can increase lead time due to lost productivity.
• Poor Communication — Lack of collaboration can result in misunderstandings and delays in the development process.
• Unclear Requirements — Ambiguity in project requirements can lead to rework and extended lead times.
• Inefficient Issue Tracking — Poorly managed issue tracking systems can lead to lost or forgotten tasks, duplicated efforts, and delays in addressing issues, ultimately extending lead times.
• Lack of Automated Testing — Insufficient automated testing can lead to manual testing bottlenecks, delaying the integration and deployment of changes.

Change Failure Rate

Change Failure Rate indicates the percentage of changes that result in failures. Symptoms affecting this metric include:

• Poor Test Coverage — Insufficient testing increases the likelihood of bugs in production.
• High Pull Request Revert Rate — Frequent rollbacks suggest instability in the codebase, indicating a high change failure rate.
• Lightning Pull Requests — Rapid submissions without adequate review can introduce errors and increase failure rates
• Inadequate Incident Response Procedures — Poorly defined processes can lead to higher failure rates during deployments.
• Knowledge Silos — Lack of shared knowledge within the team can lead to mistakes and increased failure rates.
• High Code Quality Bugs — Frequent bugs in the code can indicate underlying quality issues, raising the change failure rate.
• Lack of Feature Flags — The absence of feature flags can make it difficult to roll back changes or experiment with new features, increasing the risk of failures in production.
• Insufficient Monitoring and Alerting — Inadequate monitoring and alerting systems can make it challenging to detect and respond to issues in production, leading to prolonged failures and increased change failure rates.

Mean Time to Restore Service

Mean Time to Restore Service measures how long it takes to recover from a failure. Symptoms impacting this metric include:

• High Technical Debt — Complexity in the codebase can slow down recovery efforts, extending MTTR.
• Recurring High Cognitive Load — Overburdened team members may take longer to diagnose and fix issues.
• Poor Documentation — Lack of clear documentation can hinder recovery efforts during incidents.
• Inconsistent Incident Management — Variability in handling incidents can lead to longer recovery times.
• High Rate of Production Incidents — Frequent issues can overwhelm the team, extending recovery times.
• Lack of Post-Mortem Analysis — Not analyzing incidents can prevent learning from failures, which can result in repeated issues and longer recovery times.
• Insufficient Automation — Lack of automation in incident response and remediation processes causes manual, time-consuming troubleshooting, extending recovery times.
• Inadequate Monitoring and Observability — Insufficient monitoring and observability tools can make it difficult to quickly identify and diagnose issues in production which further delay the restoration of service.
• Siloed Incident Response — Lack of cross-functional collaboration and communication during incidents lead to delays in restoring service. As team members may not have a complete understanding of the issue or the necessary context to resolve it swiftly.

Improve your DORA Metrics using Typo

Software analytics tools are an effective way to measure DORA DevOps metrics. These tools can automate data collection from various sources and provide valuable insights. They also offer centralized dashboards for easy visualization and analysis to identify bottlenecks and inefficiencies in the software delivery process. They also facilitate benchmarking against industry standards and previous performance to set realistic improvement goals. These software analytics tools promote collaboration between development and operations by providing a common framework for discussing performance. Hence, enhancing the ability to make data-driven decisions, drive continuous improvement, and improve customer satisfaction.

Typo is a powerful software engineering platform that enhances SDLC visibility, provides developer insights, and automates workflows to help you build better software faster. It integrates seamlessly with tools like GIT, issue trackers, and CI/CD systems. It offers a single dashboard with key DORA and other engineering metrics — providing comprehensive insights into your deployment process. Additionally, Typo includes engineering benchmarks for comparing your team’s performance across industries.

Setup your Free DORA Dashboard

Conclusion

DORA metrics are essential for evaluating software delivery performance, but they reveal only part of the picture. Addressing underlying issues affecting these metrics such as high deployment frequency or lengthy change lead time, can lead to significant improvements in software quality and team efficiency.

Use tools like Typo to gain deeper insights and benchmarks, enabling more effective performance enhancements.