Coding Time is a key metric in software development that measures the time a team or developer spends exclusively writing code. This metric is essential for evaluating team productivity and efficiency during the active development phase. However, to truly understand its importance, we first need to analyze what it encompasses and how it impacts the overall development process.
What is Coding Time?
Coding Time refers to the period between when a developer begins implementing a feature or technical solution (i.e., writing code) and when that implementation is ready for review or testing. This metric excludes other phases such as:
Initial requirements analysis.
Planning meetings or technical design sessions.
Code reviews or subsequent testing.
In essence, it’s the “hands-on” time spent turning ideas into functional lines of code.
Why is it important to measure Coding Time?
Measuring Coding Time allows development teams to better understand how much time they’re actually spending coding, identify improvement opportunities, and optimize workflows. Some key benefits of tracking this metric include:
Identifying blockers: If Coding Time is significantly less than total development time, it could indicate that developers are spending too much time on non-technical tasks or facing frequent interruptions.
Improving time estimation: Knowing how long coding actually takes helps with better planning and setting more realistic expectations with stakeholders.
Increasing transparency: It provides clear data on how much effort the team is dedicating to critical development tasks.
Encouraging focus: It helps teams prioritize technical tasks and minimize distractions.
How does Teambit measure Coding Time?
Coding Time starts when a task moves to “In Progress” status or when the first commit is made. To capture the beginning of this subcycle as early as possible, the earlier of these two events is considered.
However, this approach also presents some challenges:
Pull Requests not linked to a task: Sometimes, a Pull Request (PR) isn’t clearly associated with a task, making it harder to use the transition to “In Progress” as the starting point. This can result in inconsistencies in tracking.
PRs with a single commit: When a PR includes all changes in a single commit, and the developer immediately submits the PR for review, the Coding Time may appear very short. In these cases, the metric might not accurately reflect the actual time spent coding.
To address these scenarios, Teambit complements this data with an estimated coding duration based on historical data about the developer’s average coding speed. This estimation provides a more accurate view of the time invested, even when recorded data doesn’t fully reflect the reality of the work done.
These challenges highlight the importance of understanding that Coding Time is an approximation and should be analyzed alongside other metrics to gain a complete and realistic picture of the workflow.
Best practices for improving Coding Time measurement
While measuring Coding Time can be challenging in some situations, adopting certain best practices can help you gather more accurate and meaningful data. Here are some key recommendations:
Properly link PRs to tasks (when possible):
Associate Pull Requests (PRs) with the corresponding tasks in your project management tools. This not only facilitates progress tracking but also ensures that the start of Coding Time is correctly recorded when the task moves to “In Progress.”
Keep tasks up-to-date:
Regularly update tasks in your management tool, especially when their status changes. A well-managed workflow ensures that Coding Time data aligns with actual progress.
Break down code into smaller commits:
Instead of making one large commit that covers all work, aim to divide the code into smaller, meaningful commits. This better reflects actual development progress and improves traceability.
Avoid rushing PR creation:
When possible, refrain from creating PRs immediately after a single commit. Take time to review and consolidate your work before submitting it for review. This helps ensure Coding Time reflects the actual time spent writing code.
Regularly review and adjust processes:
Periodically evaluate your workflows and the accuracy of Coding Time metrics. Identify patterns or areas for improvement, such as reducing downtime or avoiding interruptions during coding sessions.
Separate non-technical tasks from commits:
Clearly distinguish between technical activities (writing code) and non-technical ones (e.g., updating documentation or making configurations). This allows for more accurate tracking of time spent purely on coding tasks.
Encourage consistency within the team:
Ensure the entire team follows consistent processes for committing code, creating PRs, and logging tasks. This improves data accuracy and makes it easier to identify trends.
Conclusion
Coding Time isn’t just a metric for measuring productivity—it’s also a window into the health of your team’s workflows. By measuring and optimizing this time, you can ensure your team stays focused on what really matters: building high-quality software.