Git Analytics in Software Development

GIT analysis

Git Analytics in Software Development: Four Important Metrics to Assess and Improve Team Performance

Software development teams must understand the importance of efficiency, so they can meet deadlines and deliver the best software solutions to customers. But evaluating quality and productivity in software development is not easy. Because of this, team leaders and engineers are looking for ways to objectively evaluate a project or team’s status. The best way to find these ratings is the code itself. Engineering managers and executives are looking at git analytics as a quality metric to assess and improve the performance of a software team. 

Git analytics tools monitor each source of code change during the software development process. The metrics system of Git analyzes software engineers’ work and the value they make, allowing them to utilize this data to increase productivity. The following are the important Git analytics metrics:

Cycle Time

This metric refers to the period from the first work commitment of engineers to the code deployment. Shorter cycle times mean a great development process, made possible because of talented engineers, as well as effective automation and communication. 

Coding Time

This refers to the time engineers spend coding in the cycle time. This covers the time when engineers start working on an ask and make a pull request. When coding times are shorter, developers get precise and clear business requirements, which let them develop the required functionality as quickly as possible. 

Pull Request (PR) Pick-Up Time

This refers to the amount of time necessary to review PRs. If PRs sit for a long time before being examined, this could mean process inefficiencies. This can happen when engineers may be handling a heavy workload and cannot devote enough time to review codes. Or if engineers have time, they may not have the motivation to examine the PRs. Also, PR size and complexity may deter reviewers. 

Rework Rate

Rework happens when editing of a code takes place within three weeks of the initial merge. Higher rework could point to modules that might be leading to repeat problems.  Repeat rates can also be high when engineers are not familiar with the domain or technology stack. 

Development teams must address the main cause of rework. While some causes have to do with individual developers, it is important to concentrate on factors that influence the whole team or company. For instance, if the cause of a high rework rate is the inability of developers to get precise requirements, this issue impacts the entire team. Product teams need to get, translate, and hand off requirements to engineers. Any inefficiencies in this process can cause a high rework rate.

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