In the DevOps environment, tools like GitHub, Jira, or Jenkins generate a massive amount of data throughout the software lifecycle. From this information, Google’s DORA metrics are calculated (change lead time, deployment frequency, change failure rate, and mean time to recovery), which measure the efficiency and stability of the development process.
However, these metrics provide a limited view: they describe what happened, but not why. That’s where CodeSight comes in, a solution built with process mining technology that analyzes data generated by development tools to reconstruct how workflows actually unfold.
This approach makes it possible to identify bottlenecks, rework, and deviations, and to understand the underlying causes that directly influence DORA metric outcomes. For instance, when change lead time increases, CodeSight helps examine critical phases such as code review or continuous integration. If deployment frequency drops, the tool can reveal what percentage of developments never reach production or which team members concentrate a higher number of canceled pull requests.
It can also correlate a high change failure rate with the absence of prior code reviews, or uncover why the mean time to recovery increases in certain cases, such as recurring test failures or issues with hotfixes.
Additionally, these insights can be complemented with a dashboard of indicators that monitors processes in real time. This panel turns DORA metrics into operational, actionable intelligence, helping teams prioritize improvements, anticipate problems, and enhance software quality.
Ultimately, CodeSight complements the DORA metrics with an objective analysis of DevOps processes, providing a deeper understanding of the development lifecycle and enabling data-driven decisions to improve efficiency, stability, and continuous delivery.
