Cycle Time: A Detailed Guide With Challenges and Best Practices

Cycle time measures how long it takes to complete a task or produce a unit of work, providing insights into bottlenecks and areas for workflow improvement. Without understanding cycle time, organizations risk overestimating their capacity, missing deadlines, or wasting resources.

Many organizations use cycle time to improve processes, meet deadlines, and help avoid inefficiencies that often slow production and increase costs.

What Is Cycle Time?

Cycle time in software development is the period between the first code commit and its release to users. It measures how quickly a coding task moves from start to deployment in production.

It is divided into four parts:

• Coding Time: The time spent writing the code.
• Pickup Time: The time it takes for someone to start reviewing the code.
• Review Time: The time used to check the code and provide feedback.
• Deploy Time: The time it takes to release the code into production.

This metric is crucial for development teams to evaluate their efficiency and identify ways to improve their workflow for faster, high-quality software delivery.

The calculation of cycle time can vary. Some teams measure it from the first code change to its deployment, while others track it from when the code is written to when it is merged. It essentially captures how long the code remains “in progress.”

Optimizing cycle time helps development teams and supports overall business success by streamlining processes and improving efficiency.

Note

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Why Calculating Cycle Time Is Important?

Understanding cycle time is essential for evaluating your team’s performance and the effectiveness of tools like code review systems, automated testing, and deployment scripts. Cycle time measures how long it takes for your team to resolve an issue from beginning to end.

Modern CI/CD pipelines enable teams to implement updates frequently, sometimes even daily. Cycle time helps measure how quickly individual tasks are completed and allows teams to identify delays in the workflow.

When cycle time is long, such as weeks or months, it may indicate issues within the development process. Delays between starting a task and deploying it can slow down your application’s pace, making it harder to deliver updates and new features to users.

Long cycle times can also negatively impact team morale. If it takes days of hard work to release a change, developers may become frustrated and dread the process. This negative experience can reduce productivity and lead to burnout or higher turnover rates. By monitoring cycle time, teams can gain insights into areas where processes can be optimized.

While there are many software metrics for measuring development performance, such as defect count and story points, cycle time stands out because it highlights a broader range of issues in the workflow. Understanding cycle time helps teams better predict delivery timelines.

Here are a few questions cycle time can help answer:

• Is your team breaking tasks into manageable pieces?
• Are code reviews happening quickly, or is there a delay?
• Are there issues with your pipeline tools or processes?
• Is the operations team quickly building servers for production, or are they causing delays?
• How long does the quality assurance cycle take, and are there bottlenecks in that process?

How to Measure Cycle Time?

To measure cycle time, track the start and end times for each task, subtracting any unproductive time. Calculating the average cycle time and other key metrics is essential to ensure the software meets its requirements and aligns with its end goals.

Below is how you can measure cycle time:

1. Calculate the Real Time Spent on Work: Determine the actual time your team spends on tasks by deducting unproductive time, such as breaks, meetings, or distractions, from the total work hours. The remaining time represents the effective time dedicated to tasks.
2. Track Each Task: For every task, record the start and end times. This can be done manually or using project management tools like Jira, Trello, or GitHub, which automatically track this information.
3. Calculate Cycle Time for Each Task: To calculate the cycle time of a task, subtract the start time from the end time.

Cycle Time = End Time – Start Time

4. Calculate Average Cycle Time: To evaluate overall team performance, calculate the average cycle time for a specific period (e.g., weekly or monthly).

Average Cycle Time = (Total of all task cycle times) / (Number of tasks)

5. Use Automation Tools: Many project management tools, such as Jira and Trello, offer built-in reports that automatically calculate and track cycle time. These reports help identify trends and inefficiencies.

Example: Calculating Cycle Time for One Task:

Start Time: Monday at 9:00 AM
End Time: Wednesday at 3:00 PM

Steps to calculate cycle time:

1. Convert the start and end times to the same unit (hours).
2. Calculate the total time:
• Monday 9:00 AM to Tuesday 9:00 AM = 24 hours
• Tuesday 9:00 AM to Wednesday 9:00 AM = 24 hours
• Wednesday 9:00 AM to Wednesday 3:00 PM = 6 hours
3. Add the total time: 24 + 24 + 6 = 54 hours
4. Cycle Time for the task = 54 hours

Example with Multiple Tasks

Task 1: 40 hours
Task 2: 60 hours
Task 3: 30 hours

• Add the cycle times: 40 + 60 + 30 = 130 hours
• Divide by the number of tasks: 130 ÷ 3 = 43.33 hours
• Average Cycle Time for these tasks = 43.33 hours

You can repeat this process to track and optimize cycle time across all tasks over time.

Now that you have learned some metrics for calculating cycle time, it is important to understand related terms that provide a broader perspective on the entire process. Additionally, exploring cycle time-related concepts will help you cover other aspects of the Software Development Life Cycle (SDLC).

Cycle Time-Related Terms

Cycle time-related terms provide a deeper understanding of various factors influencing the efficiency of a process. They help identify bottlenecks and optimize the software development process.

Here are some of the cycle time-related terms mentioned below:

• Wait Time: This is the time a task or item spends waiting before it can be worked on or moved to the next step. It represents the idle time before the next action is taken.
• Move Time: This is the time it takes to move a task or item from one step or place to another. It could involve physical movement or the time it takes for tasks to transition between teams or systems.
• Process Time: This is the actual time spent working on a task to complete it. It refers to the hands-on time needed to finish a specific part of the process.
• Little’s Law: This formula illustrates the relationship between the number of tasks in progress (WIP), the speed at which tasks get done (throughput), and how long it takes to complete them (cycle time).
• Throughput: This is the rate at which tasks or items are completed. It shows how many tasks get finished in a given amount of time, which is the rate at which work is completed.
• Work In Progress (WIP): This refers to the number of tasks or items that are still being worked on and have not been finished yet. It represents the ongoing work at any given moment.
• Lead Time: This is the total time taken from the moment a request or task is initiated until it is delivered to the customer. It includes both wait time and cycle time, providing a broader view of the end-to-end process.
• Takt Time: This represents the pace at which a product or service must be completed to meet customer demand. It is calculated by dividing the available working time by the number of units required.
• Queue Time: This refers to the amount of time a task spends waiting in a queue before being processed. It is a key factor in increasing overall cycle time and often results from resource constraints or inefficiencies.
• Touch Time: This is the actual time a team member is actively working on a task. It is similar to process time but focuses more on manual or human effort rather than automated processes.
• Blocked Time: This refers to the time when a task is unable to proceed due to dependencies, resource unavailability, or other obstacles. Reducing blocked time can help lower overall cycle time.
• Completion Time: This is the total time taken from the initiation of a task to its successful completion, including all stages of processing, waiting, and transitioning.
• Flow Efficiency: Flow efficiency is the ratio of process time to the total cycle time, expressed as a percentage. It measures how efficiently work flows through the system.
• Cycle Time Efficiency: This metric compares the ideal cycle time (i.e., only active work time) to the actual cycle time, helping teams assess process inefficiencies.

Cycle Time vs Takt Time vs Lead Time

Cycle time is the total time required for a task or product to move through the entire production process, from start to finish, including all stages of work.

Takt time is essentially a planning tool that helps determine the production pace required to meet customer demand. It is primarily focused on the production process and ensures that work is done at the right speed to meet demand.

On the other hand, lead time is focused on the entire process, from order placement to product delivery. It measures the time taken for a product or service to reach the customer and encompasses all delays, such as waiting for materials, processing, shipping, etc.

Since takt time and lead time go hand in hand, understanding the difference between these two and how they differ from cycle time is important.

Understanding these metrics helps teams optimize workflows and align with customer expectations better.

Challenges in Reducing Cycle Time

Reducing cycle time can be tough because of several factors that affect efficiency and workflow.

Here are some of the factors:

• Bottlenecks in Processes: Certain steps in the workflow, such as delays in code reviews or testing, can slow everything down and lengthen the overall process.
• Inconsistent Workflows: When processes are not standardized, tasks take varying amounts of time, making it harder to identify and address inefficiencies.
• Limited Automation: Relying on manual work for repetitive tasks like testing or deployment can lengthen cycle time compared to using automation.
• Overloaded Teams: When teams are overburdened or lack sufficient resources, tasks take longer to complete.
• Ineffective Communication: Poor coordination between teams, such as developers, testers, and operations, can lead to confusion and slow handoffs.
• Insufficient Metrics and Tracking: Without proper tools to monitor and track progress, it becomes difficult to measure cycle time accurately or identify areas for improvement.
• Technical Debt: Old systems, outdated code, or unresolved issues can slow down development and increase the need for debugging or rework.
• Waiting on Dependencies: When tasks depend on other teams or external systems, delays on their end can lead to idle time.
• Resistance to Change: Teams may hesitate to adopt new tools or processes, even if they could help speed things up.
• Unclear Prioritization: When tasks are not clearly prioritized, resources may be spent on less important work, delaying critical tasks.
• Complex Approval Processes: Too many approval steps or redundant reviews can introduce unnecessary delays.

To overcome these challenges, teams must optimize processes, adopt better tools, improve collaboration, and continuously track progress to ensure lasting improvements.

Cycle time primarily focuses on the software development process. While development is a key part, testing often acts as a critical checkpoint where potential issues are identified and resolved. Efficient testing ensures that the quality of the code is validated quickly, allowing the team to move through the pipeline without unnecessary delays.

To ensure this, you can use cloud-based platforms that allow you to validate and test every aspect of the software to maintain its quality.
One such cloud-based platform is LambdaTest, an AI-powered test execution platform offering features like HyperExecute to help reduce cycle time by enhancing the speed and efficiency of test execution. It achieves this by enabling parallel test execution across a scalable, cloud-based infrastructure, which can accelerate testing by up to 70%. This rapid execution allows teams to run more tests concurrently, maximize the feedback for developers and shorten the time taken to identify and resolve issues.

Additionally, its AI-powered capabilities, such as auto-healing and intelligent wait mechanisms, help eliminate bottlenecks, ensuring consistent and fast test execution.

Best Practices to Improve Cycle Time

Below are key tips to help you improve cycle time, allowing you to better streamline each phase of the development process and help you focus on optimizing testing, using automation tools and more.

• Automate Repetitive Tasks: Automation can speed up tasks by handling work that doesn’t require human involvement. A CI/CD pipeline can manage integrations and deployments, while automated end-to-end testing saves time compared to manual testing. The more tasks you automate, the faster the process becomes.
• Use Reusable Components: As your product evolves, create a library of reusable components. This eliminates the need to redo common elements like buttons or forms. Start with the most-used components and allow your team time to develop and document them. It may feel slow at first, but it will save time later.
• Simplify Code Reviews: Code reviews can cause delays, but you can speed things up by making a few adjustments:
• Time to First Review: Review pull requests quickly after submission to reduce waiting time.
• Time to Approval: Keep pull requests smaller and reduce the number of comments needed for approval. Adjust the number of approvals required based on the project stage.
• Time to Merge: After approval, ensure the code is merged promptly. Automated CI/CD pipelines can streamline this step.
• Improve Testing Steps: Streamline automation testing to make it quick and reliable. Test earlier in the process to identify issues sooner and save time later. Involve QA specialists in reviewing designs and specifications before coding begins. Encourage everyone on the team to double-check their work to avoid introducing errors.
• Analyze Delays: Identify tasks that took longer than expected and determine the reasons. Were they too large, delayed in review, or slowed by outdated systems? Addressing these causes will help smooth out the process.
• Address Technical Challenges: Unresolved bugs, outdated systems, or missing documentation can slow progress. Discuss these challenges regularly with the team, set limits on how much of this work can be postponed, and focus on resolving them to maintain efficiency.
• Work on Smaller Tasks: Breaking large tasks into smaller, manageable units makes them quicker to complete and easier to handle. Start by reducing the size of the largest tasks over time and continue shrinking them until they reach a comfortable size for your team.
• Test New Approaches: Experiment with different ways to improve the process and observe their impact. For example, setting reminders for pull requests helped one team review faster, leading to quicker task completion. Small adjustments can lead to significant improvements.

Conclusion

Understanding cycle time is crucial for any business that relies on processes. It helps identify how efficiently tasks or production are being completed, making it easier to spot areas that need improvement. By tracking cycle time regularly, companies can see where delays are happening and address bottlenecks. This can guide decisions on how to allocate resources, distribute workloads, and optimize processes.

Keeping an eye on cycle time can boost productivity by ensuring smoother operations. It also helps teams set realistic goals and deadlines, ensuring projects are completed on time. Additionally, cycle time is a great way to measure performance and drive ongoing improvements. By focusing on cycle time, businesses can reduce delays, improve quality, and maintain consistency, leading to better results overall.

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Nazneen Ahmad

Nazneen Ahmad is an experienced technical writer with over five years of experience in the software development and testing field. As a freelancer, she has worked on various projects to create technical documentation, user manuals, training materials, and other SEO-optimized content in various domains, including IT, healthcare, finance, and education.

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