Our Blog

Engineering Managers have a variety of metrics they can draw from to assist in understanding their teams' efficiency & operational performance. What’s missing is a precise measurement of risk (i.e. complexity) and Resilience (i.e. mitigation effort).

Our predictive model highlighted the risks leading to a CVE in Redis, showing the importance of objective risk assessment over traditional methods. This blog delves into the misconceptions about small pull requests and conventional mitigation's limitations

Engineers experience cycle time through risk and complexity trade-offs. Shepherdly helps teams measure this and codify their risk tolerance.

Analyzing six popular open-source repositories, Shepherdly examined how various established and novel quality KPIs are reflected in some of the most extensively used software tools.

From ChatGPT to GitHub Copilot, generative AI is here, and with it has come scores of new technology that’s making it easier than ever for developers to complete coding tasks faster, innovate, and create better software.

In addition to predicting the likelihood of a PR causing a bug, Shepherdly adds even more context to the bug hotspots in your code when modified.

As a software engineer, you may have shipped something that you and the reviewers knew was risky, but it was too expensive to investigate the issue further. You may have added remediation steps, but time pressure may have prevented you from doing so.