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Agile development methodologies are widely adopted by development teams. These methodologies enable you to produce software faster, more efficiently, and at a lower cost. Provided, of course, that agile is implemented successfully. Unfortunately, many teams struggle to adopt agile, missing out on the benefits it can provide. 

Before deciding that agile isn’t right for your team or projects, you can try refining your implementation with metrics. Integrating agile metrics into your sprints can help you identify possible issues and areas for improvement. In this article, you’ll learn what agile metrics are, how to collect metrics data, and how to apply this data to improve your development processes. 

What Are Agile Metrics?

Agile metrics are quantifiable measures used to track the progress of agile workflows. Like traditional metrics, agile metrics are used to measure productivity and effectiveness. Unlike traditional metrics, agile metrics often focus on user needs and expectations. Teams can use these metrics to monitor and improve workflows and assess software quality.

There are several principles that agile metrics follow:

  • Metrics are collected and used by teams

  • You should use metrics for a particular issue or experiment

  • The metrics you use should align with your goals

  • Metrics should require minimal work to collect

How to Collect Metrics Data

Collecting metrics data isn’t always straightforward. You need to choose measures that can be collected reliably and add value to your processes. Additionally, any measures you use should be evaluated in context. A single metric cannot present you with a reliable picture of your processes or issues. 

When collecting data, take the time to verify that you are measuring accurately. If you use incorrect values or partial data, your metrics are less representative and therefore less valuable. You are sure to waste time and effort if you base changes on inaccurate metrics. 

Whenever possible, try to automate data collection. Gathering metrics should not add a significant amount of work to your processes. If it does, those metrics may not be worthwhile to measure. 

Take advantage of any tools you are already using that have logging and monitoring features. Pulling data from existing reports can save you time and ensure that collection of metrics data is consistent.

Using Agile Metrics to Improve Development

Knowing which metrics to use and how to apply your results to improve your systems can take time and practice. Below are some metrics you should consider along with some insights that you can gain from these measures.


Workflow metrics typically relate to timing and productivity. These measures can cover sprints, epics, or individual issues. Sprints are the periods or iterations your project is divided into. Epics are your individual projects or major versions.

Lead and Cycle Time

Lead time and cycle time are two metrics that you can use together to improve your workflows. Lead time measures the gap between when a task is introduced until it is completed. Cycle time measures the time spent actually working on tasks. 

Consistent cycle times enable you to better predict iteration timing. Long lead times with short cycles times can indicate that work is being blocked by approval processes or other tasks. It can also mean that your team is taking on too much work for a single sprint or that tasks are sitting in your backlog. 

Watch for outliers in your lead and cycle times, either very short or very long. These instances can give you insight into what your team is struggling with vs what is easy for them.

Cumulative Flow

Another useful metric for improving workflow is cumulative flow. This metric provides a visualization of overall project progress. It incorporates data from cycle, throughput, and work in progress metrics.

Cumulative flow shows the path from task introduction to completion broken down by phase in your process. It can be used to quickly identify issues such as growing backlogs or to identify opportunities for adding more features. 

Product Quality

Measuring software quality can be a significant challenge. Even if your product is technically flawless, it is of low quality if it doesn’t meet your customers’ needs. 

Number Escaped Defects

This metric measures the number of defects found after a product is released into production. Defects include those found by your team as well as those reported by users and third-parties. 

If you are releasing products with high numbers of escaped defects, you may need to adjust your testing methods. Start by checking that your testing is comprehensive and reliable. Also, pay special attention to the environments that defects are reported for. It’s possible that defects are being reported for devices or systems you never intended to support.

Net Promoter Score (NPS)

NPS is another metric you can use to measure product quality. NPS measures how likely users are to recommend a release to others. You can use it to gauge customer satisfaction and loyalty. If your NPS is low, you may be misunderstanding what functionality or features your customer needs. 

A/B testing can help you decide how your product can be improved both before and after release. In addition, before release, try to ensure that you clearly understand your customers’ expectations. You may find that you’re understanding features completely different than what they intend due to technical language barriers.

Code Quality

Although there is some overlap between product and code quality, these aspects are not identical. You can have a high quality product with low quality code and vice versa. For example, your software may do everything your customer wants while being based on code that is difficult to read or modify.

Cyclomatic Complexity 

Cyclomatic complexity measures the number of independent paths in your codebase. The more paths you have, the more complex your code. 

High complexity code should be avoided when possible since it is more difficult to test and maintain. To reduce complexity, make sure you’re using variables instead of hardcoding values. You should also try to break down actions into discrete, reusable functions when possible. Doing so can help reduce repetition in your code and ease later modifications.

Defect Category

Defect category measures the ratio of defects by type according to the number of lines in your codebase. You can use whatever categories you want, as long as you define groupings clearly. Some typical categories include functionality, usability, compatibility, performance, and security. 

Defect categories can highlight flawed or inconsistent coding practices. Additionally, if you aren’t seeing instances of a category that you expect to see, it might be an indication that your testing is flawed. 

Team Health

When evaluating your agile implementation, it is important not to overlook your team. Agile workflows depend on autonomy and collaboration, neither of which occur when teams are dissatisfied. 

Here are two metrics you can use to gauge the comfort of your team:

  • Team happiness—based on a self-reported scale and open-ended questions. For example, “What would increase your happiness” or “What is the main cause of your unhappiness”. 

  • Team morale—based on self-reported scale. It includes statements such as “I feel fit and strong in my team” or “I am proud of the work that I do for my team”.


If your team is struggling and you can’t identify why these metrics might help. Depending on the results you receive, you may find that your team has resistance or agile. Or, they may simply need more guidance or support. 

If these metrics aren’t supplying specific recommendations from your team, consider adding more open-ended questions. Alternatively, keep in mind that team happiness isn’t always related to work. Your team members have individual lives that affect them even when work environments are ideal.


Regardless of which metrics you choose, make sure you use your data objectively. Any metrics you select should be used by the team as a whole and reviewed at each retrospective. If only a select few are reviewing metrics data you are unlikely to gain meaningful insights. Likewise, if metrics are being collected but not discussed you cannot gain value from your data. 

Metrics are most effective when you compare measures against a baseline. A baseline gives you the ability to detect trends in data and enables you to more easily identify if something is an issue. When creating this baseline, make sure that you are only comparing projects of similar value and complexity. 

Hopefully, this article helped you better understand how to effectively collect and apply metrics to improve your development cycles. Keep the tips covered here in mind when analyzing your metrics and don’t be afraid to drop or add metrics as needed. These measures are designed to improve your processes, not impede productivity.

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