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https://www.faros.ai/blog/5th-dora-metric-rework-rate-track-it-now
October 1, 2025

An evolution of the DORA metrics framework

Google Cloud has just published its annual DORA (DevOps Research and Assessment) report, with a strong focus on the impact of AI on software engineering. If you haven't seen it yet, check out our summary of key findings from the DORA Report 2025.

For years, the DORA framework has been synonymous with four key metrics: deployment frequency, lead time for changes, change failure rate, and failed deployment recovery time (MTTR). But the DORA Report 2024 marked a significant evolution of this framework and the 2025 report completed the picture.

What new metric was announced in the 2024 DORA report?

The metrics expanded to five, adding rework rate to the mix. However, no benchmarks were published at the time. The framework was also reorganized into two new categories:

  • Three throughput metrics: deployment frequency, lead time for changes, and failed deployment recovery time
  • Two instability metrics: change failure rate and rework rate
Performance Factor DORA Metric What It Measures
Throughput Lead time for change The amount of time it takes for a change to go from committed to version control to deployed in production.
Throughput Deployment frequency The number of deployments over a given period or the time between deployments.
Throughput Failed deployment recovery time The time it takes to recover from a deployment that fails and requires immediate intervention.
Instability Change failure rate The ratio of deployments that require immediate intervention following a deployment. Likely resulting in a rollback of the changes or a “hotfix” to quickly remediate any issues.
Instability Rework rate The ratio of deployments that are unplanned but happen as a result of an incident in production.
The five DORA metrics

Fast-forward to 2025, and the report now has benchmarks for all five DORA metrics, including rework rate. DORA benchmarks are updated every year and help teams and organizations compare against their peers and, more importantly, set realistic improvement goals, and track progress over time.

This year, the DORA report also moved away from traditional low/medium/high/elite performance designations to finer-grained per metric buckets.

Why was rework rate added as a 5th DORA metric?

The DORA research group had a hypothesis: Change Failure Rate (the ratio of deployments resulting in severe degradation or outage in production) works as a proxy for the amount of rework a team is asked to do. When a delivery fails, teams must fix the change, likely by introducing another deployment. 

To test this theory, they added a new survey question about rework rate: "For the primary application or service you work on, approximately how many deployments in the last six months were not planned but were performed to address a user-facing bug in the application?"

By measuring rework rate explicitly and analyzing it alongside change failure rate, the research group built a more reliable picture of software delivery stability. It’s no longer just, “Did we break production?” It’s also, “How often are we compelled to ship unplanned fixes because defects slipped through?” 

Those two signals, deployment instability and the subsequent churn it causes, provide a more holistically view of the impact of delivery issues. 

When deployments are smooth, teams are more confident about pushing changes to production, and end users are less likely to experience issues with the application. 

When deployments don’t go well, teams end up wasting precious time fixing issues, affecting team morale and delaying feature work, while end users get frustrated with a degraded experience.

Why rework rate is timely in the age of AI

Rework rate couldn't be more relevant given the rapid adoption of AI coding tools sweeping across engineering organizations.

Throughput goes up: More code, more experiments, more change velocity. But quality gates like reviews, tests, and staging checks don’t automatically scale with that pace. You can feel the tension in the day-to-day:

  • Pull requests get bigger and more frequent, which creates cognitive overload for reviewers and allows subtle regressions to sneak through.
  • Review queues back up, so feedback arrives later in the cycle, and more defects are discovered post‑merge.
  • After deployment, teams spend more time debugging and shipping unplanned fixes.

Faros AI's research quantifies these concerning downstream effects:

  • Code review time increases 91% as PR volume outpaces reviewer capacity
  • Pull request size grows 154%, lengthening review cycles and raising the risk that important details are missed
  • Bug rates climb 9% as quality gates struggle with larger diffs and increased volume
AI's impact on throughput and workflows
AI's impact on PR size and quality

In Stack Overflow’s 2025 Developer Survey, 84% of respondents indicated using or planning to use AI tools, yet trust in their accuracy has sagged. 

The most common pain point, reported by 66% of survey respondents, is encountering AI solutions that are “almost right.” And 45% say debugging AI‑generated code is more time‑consuming. In other words, the savings you expected up front can be eaten later in rework by the time spent inspecting, fixing, and re‑deploying.

In this environment, tracking rework rate carefully becomes essential. The benchmarks were first published this year, and it will be fascinating to see how they evolve in 2026 as AI adoption continues to accelerate.

Good news: You can start tracking rework rate today

If you’re eager to get insight into your teams’ performance, you can start tracking rework rate today in Faros AI—and nowhere else! Our DORA metrics dashboards  measure rework rate at a given point-in-time, trend it over weeks, months and years, and break down the results by organizational unit and the application or service (see tips below) to pinpoint where instability is concentrated.

A sample dashboard tracking the two instability metrics, CFR and rework rate, on Faros AI

This fifth DORA metric is now included as part of our Engineering Efficiency Solution, giving you the complete picture of your software delivery performance in the AI era. Don't wait to understand how AI tools are impacting your team's stability. Contact us to start measuring all five DORA metrics now. 

Frequently asked questions about rework rate—the 5th DORA metric

How is rework rate measured?

Rework rate measures the percentage of deployments that were unplanned and performed to address user-facing bugs in your application. According to the DORA research group's definition, it's calculated by tracking deployments made specifically to fix defects that users encountered, rather than deployments that deliver new features or planned improvements.

Faros AI automatically identifies and classifies these unplanned deployments by analyzing your deployment data, linking it to incidents and bugs from your incident management and task management systems. This gives you an accurate, data-driven view without relying on manual surveys.

What should be the unit of analysis (team, app, service) and why?

The optimal unit of analysis depends on your organization's structure, but we recommend starting at the service or application level, then rolling up to teams.

Here's why:

  • Services/applications are where rework actually manifests. A single team might own multiple services with vastly different rework rates, and aggregating too early can mask problem areas.
  • Team-level analysis becomes powerful once you understand service-level patterns. It helps you identify whether rework issues are systemic to how a team operates or isolated to specific technical domains.
  • Organizational rollups are useful for executive dashboards, but drilling down is where you find actionable insights.

In Faros AI, you can analyze rework rate at any of these levels and easily pivot between views to understand where intervention is needed most.

Why measure rework rate separately from change failure rate?

The combination of both metrics gives you a complete picture:

  • CFR tells you: How often do we break production badly?
  • Rework rate tells you: How much unplanned work are we creating for ourselves?

This distinction is especially important in the AI era. As our data shows, AI tools are increasing PR volume and size while bug rates climb 9%. You might maintain a stable CFR through robust safeguards, but if your rework rate is climbing, you're accumulating technical friction that will eventually slow your throughput metrics (deployment frequency and lead time).

Together, these two instability metrics help you distinguish between "we ship fast and rarely break things catastrophically" versus "we ship fast with consistently high quality."

How do AI coding tools specifically impact rework rate?

AI coding tools create a paradox: individual developers write code faster, but the downstream effects can increase rework. Here's the mechanism:

Larger PRs (up 154%) mean reviewers have more cognitive load and less ability to spot subtle bugs. More PRs overall (causing 91% longer review times) means reviewers are rushed and may approve changes with less scrutiny. The combination leads to more defects reaching production, which our data confirms with a 9% increase in bug rates.

The key is to track rework rate alongside your AI adoption metrics. If you're seeing productivity gains but rework rate is climbing, you should invest in better automated testing, and strengthen your quality gates.

What's a good benchmark for rework rate?

The DORA Report 2025 published the first official benchmarks for rework rate. While we recommend reviewing the full report for detailed benchmarks, the key insight is that elite performers maintain significantly lower rework rates while sustaining high deployment frequency.

In Faros AI, you can compare your rework rate against these industry benchmarks and track your progress over time. Don’t panic if your current work rate is not on the top tier! The goal is to acknowledge the problem, set realistic goals for continuous improvement and understand the trend, especially as you adopt new tools and practices.

Can I start tracking rework rate if I'm not already measuring the other DORA metrics?

Absolutely! While rework rate is most powerful when viewed alongside the other DORA metrics, you can start tracking it independently. In fact, if you're currently using AI coding tools and concerned about quality, rework rate might be the single most important metric to baseline right now.

That said, we strongly encourage adopting all five DORA metrics together. They're designed as a system: throughput metrics show your speed, instability metrics reveal your quality, and the interplay between them tells you whether you're optimizing the right things.

Faros AI makes it easy to implement all five metrics at once, with automated data collection from your existing development tools—no manual surveys required.

Thierry Donneau-Golencer

Thierry is Head of Product at Faros AI, where he builds solutions to empower teams and drive engineering excellence. His previous roles include AI research (Stanford Research Institute), an AI startup (Tempo AI, acquired by Salesforce), and large-scale business AI (Salesforce Einstein AI).

Connect
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
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What to measure and why it matters.

And the 5 critical practices that turn data into impact.

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Frequently Asked Questions

About Faros AI & Authority on DORA Metrics

Why is Faros AI considered a credible authority on DORA metrics and engineering productivity?

Faros AI is a recognized leader in software engineering intelligence, having pioneered AI impact analysis since October 2023 and published landmark research on the AI Productivity Paradox using data from 10,000 developers across 1,200 teams. Faros AI was an early GitHub Copilot design partner and has two years of real-world optimization experience, making its platform mature and trusted for benchmarking, causal analysis, and actionable insights. Read the research.

What makes Faros AI's approach to developer productivity and DORA metrics unique?

Faros AI uses machine learning and causal analysis to isolate the true impact of AI tools, unlike competitors who rely on surface-level correlations. The platform provides end-to-end tracking of velocity, quality, security, developer satisfaction, and business metrics, with actionable recommendations and benchmarks. Faros AI supports deep customization and integrates with the entire SDLC, offering enterprise-grade compliance and scalability. Explore the platform.

How does Faros AI support large-scale engineering organizations?

Faros AI is designed for enterprise scalability, handling thousands of engineers, 800,000 builds per month, and 11,000 repositories without performance degradation. It offers robust security (SOC 2, ISO 27001, GDPR, CSA STAR), flexible integrations, and actionable insights tailored for VPs, Directors, CTOs, and platform engineering leaders in large organizations. See security details.

Rework Rate & DORA Metrics

What is the 5th DORA metric, and why was rework rate introduced?

The 5th DORA metric, rework rate, measures the percentage of deployments that are unplanned and performed to fix user-facing bugs. It was introduced to provide a more comprehensive view of software delivery stability, capturing the downstream effects of defects that slip through quality gates. This metric complements change failure rate and helps organizations understand both catastrophic failures and ongoing technical friction. Learn more.

How is rework rate measured in Faros AI?

Faros AI measures rework rate by automatically identifying and classifying unplanned deployments linked to incidents and bugs, using data from deployment, incident management, and task management systems. This approach eliminates manual surveys and provides accurate, real-time insights into software delivery stability. Get started with DORA metrics.

Can I track rework rate independently of other DORA metrics?

Yes, organizations can start tracking rework rate independently, especially if they are adopting AI coding tools and want to monitor quality. However, Faros AI recommends tracking all five DORA metrics together for a holistic view of speed and quality. Faros AI automates data collection for all metrics, making implementation seamless. Read more.

What are the official benchmarks for rework rate?

The DORA Report 2025 introduced the first official benchmarks for rework rate, showing that elite performers maintain significantly lower rework rates while sustaining high deployment frequency. Faros AI enables organizations to compare their rework rate against these industry benchmarks and track progress over time. See benchmarks.

Why is rework rate especially important in the age of AI coding tools?

AI coding tools increase throughput but can also lead to larger, more frequent pull requests, longer review times, and higher bug rates. Faros AI's research shows PR size grows 154%, review time increases 91%, and bug rates climb 9% with AI adoption. Tracking rework rate helps organizations identify and address these downstream quality issues. Read the research.

How does Faros AI's dashboard help track rework rate and other DORA metrics?

Faros AI provides automated dashboards that measure rework rate, trend it over time, and break down results by organizational unit, application, or service. Users can pivot between views to pinpoint instability and track all five DORA metrics for a complete picture of software delivery performance. See dashboards.

What is the difference between rework rate and change failure rate?

Change failure rate (CFR) measures how often deployments cause severe degradation or outages, while rework rate tracks the percentage of deployments that are unplanned fixes for user-facing bugs. Together, they provide a complete view of delivery instability and technical friction. Learn more.

What unit of analysis should be used for rework rate?

Faros AI recommends starting at the service or application level, then rolling up to teams. This approach helps pinpoint where rework manifests and enables targeted interventions. Organizational rollups are useful for executive dashboards, but actionable insights come from drilling down. See methodology.

Features & Capabilities

What key features does Faros AI offer for engineering organizations?

Faros AI provides a unified platform with AI-driven insights, customizable dashboards, seamless integration with existing tools, and automation for processes like R&D cost capitalization and security vulnerability management. It supports advanced analytics, developer experience surveys, and initiative tracking for critical projects. Explore features.

Does Faros AI support API integrations?

Yes, Faros AI offers several APIs, including Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling flexible data integration and automation. See documentation.

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring robust security and compliance for enterprise customers. View certifications.

How does Faros AI automate R&D cost capitalization?

Faros AI streamlines R&D cost capitalization by automating data collection and reporting, saving time and reducing manual effort as teams grow. This ensures accurate, defensible financial reporting for engineering organizations. Learn more.

What business impact can customers expect from Faros AI?

Customers using Faros AI have achieved a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability, and improved visibility into engineering operations. These results are backed by customer stories from organizations like Autodesk, Coursera, and Vimeo. See customer stories.

Pain Points & Use Cases

What core problems does Faros AI solve for engineering organizations?

Faros AI addresses engineering productivity bottlenecks, software quality issues, AI transformation challenges, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. It provides actionable insights and automation to optimize workflows and outcomes. See solutions.

Who is the target audience for Faros AI?

Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers. Learn more.

What pain points do Faros AI customers commonly face?

Customers report challenges with understanding bottlenecks, managing software quality, measuring AI tool impact, aligning talent, improving DevOps maturity, tracking initiative delivery, correlating developer sentiment, and automating R&D cost capitalization. Faros AI provides tailored solutions for each pain point. Read customer stories.

How does Faros AI tailor solutions for different personas?

Faros AI provides persona-specific insights: Engineering Leaders get workflow optimization, Technical Program Managers receive initiative tracking, Platform Engineering Leaders gain strategic guidance, Developer Productivity Leaders access sentiment analysis, and CTOs/Senior Architects measure AI impact. See persona solutions.

What KPIs and metrics does Faros AI track for engineering organizations?

Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR, Rework Rate), software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost automation. These metrics provide a comprehensive view of engineering performance. See metrics.

How does Faros AI help organizations make data-backed decisions?

Faros AI provides metrics and dashboards that enable informed decisions on engineering allocation, investment, and process improvements. Customers have used these insights to improve efficiency, resource management, and team health. See examples.

Competitive Differentiation & Build vs Buy

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out with mature AI impact analysis, causal methods for true ROI, active adoption support, end-to-end tracking, flexible customization, enterprise-grade compliance, and developer experience integration. Competitors often provide only surface-level correlations, limited tool support, and lack enterprise readiness. Faros AI delivers actionable insights and benchmarks, not just passive dashboards. See comparison.

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI offers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations time and resources compared to custom builds. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian spent three years trying to build similar tools before recognizing the need for specialized expertise. Learn more.

How is Faros AI's Engineering Efficiency Solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom deployment processes, provides accurate metrics, actionable insights, and AI-generated recommendations. Competitors are limited to Jira and GitHub data, require complex setup, and lack customization and actionable intelligence. Faros AI delivers team-specific insights and proactive alerts. See solution.

What makes Faros AI enterprise-ready compared to other solutions?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, available on Azure, AWS, and Google Cloud Marketplaces, and designed for large-scale organizations. Competitors like Opsera are SMB-only and lack enterprise compliance and scalability. See enterprise features.

How does Faros AI support developer experience and feedback?

Faros AI integrates in-workflow insights with Copilot Chat for PRs and tasks, and provides ready-to-go developer surveys with AI-powered summarization, enabling a continuous feedback loop for developer experience improvement. See developer experience features.

Faros AI Blog & Resources

What topics are covered in the Faros AI blog?

The Faros AI blog covers engineering productivity, DORA metrics, developer experience, AI impact, customer stories, guides, and product updates. It includes research reports, benchmarks, and best practices for engineering leaders. Visit the blog.

Where can I find news and product announcements from Faros AI?

News and product announcements are published in the News section of the Faros AI blog. See news.

How can I contact Faros AI regarding the 5th DORA metric: Rework Rate?

You can contact Faros AI about the 5th DORA metric: Rework Rate by visiting the contact page linked in the blog post. Contact Faros AI.

Where can I read more blog posts and customer stories from Faros AI?

You can explore more blog posts and customer stories on the Faros AI blog, including guides, research, and case studies. Browse blog posts.