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Gitential is  dedicated to providing software teams the best analytics and recommendation engine to build better, more sustainable code and software products. Our platform analyzes source code and its evolution in git repositories to provide deep insights to help software engineering managers improve the quality, collaboration, efficiency, and productivity of software development teams.

Software development analytics is analytics on software development data for developers and engineering managers to empower software developers and teams to gain insights from their data to make better decisions.

Measuring software development is difficult. It’s been said many times that it is the holy grail of the technology industry. The most obvious metrics, like hours worked, lines of code, bugs closed or function points all have their disadvantages. So then what metrics are recommended to be used for developers? While comparing developers on the above-mentioned metrics is mostly a bad idea, tracking them and looking at trends over time can provide valuable insights. Some other KPIs to look at can include number and frequency of commits, velocity, number of contributors, code churn, teamwork, code coverage or code complexity.

Software development analytics tools provide information for developers and engineering managers to make better decisions and optimize software development productivity. Software development analytics tools increase transparency, measurability, and enable benchmarking within and outside of development teams.

It is important to distinguish between qualitative and quantitative metrics when evaluating software development. On the quantitative side, you can look at cycle time, velocity, code volume, number and frequency of commits, number of contributors, efficiency, velocity or code churn, for example. Qualitative metrics can include team communication and team collaboration, continuous improvement, or product roadmap readiness.

Developer productivity is measurable, but it’s not easy. It’s possible to understand the effectiveness of developers and their ability to produce results with the right tools and the right data. In general, you can measure a developer’s productivity based on two metrics: (1) Measurement of work completed and (2) Measurement of quality or importance. Taken together, these two metrics show both how much and what level of work is being done.

We’re storing only the extracted git metrics on our servers. This includes the commit metadata and some calculated values. 

For example: We’re storing the filenames but not the file contents for a particular commit.

The cloned git repository used only in a temporary isolated environment during the analysis, and after the data collection it’s automatically deleted.

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