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2 years ago

AI Code Monitor Beta V3.5: Connect Azure DevOps, Repository Detail page & Code review PR detail page features

Here's the latest video release notes: https://youtu.be/mrdyp1QcvAc

In the current release, we have implemented a few customer-centric features, which are explained below.

1) Users now have the option to connect with Azure DevOps. Previously, they just had the option to connect with GitHub.

Other related How-to-video to help you connect with Azure DevOps is shared below:

https://announcekit.co/semasoftware/release-notes/connect-azure-devops-on-ai-code-monitor-tool-how-to-instructions-1CYfL2

2) As part of simplification of the user journey for the customers, we have removed the repository group information from the Dashboard and given its own placeholder. The user can directly click on the repository group link left-hand-pane to view the GenAI composition for various repository groups and the underlying repositories. In addition to that, if the user wants to view the details of that repository at the chunk & code level, they can click on any repository name, and they will be taken to the Repository detail page.

3) Additional user-friendly features on the Repository detail page. If the user wants to search for a specific folder or file, they can easily search that name. Also, they have the option to filter the chunk based on the labels: NotGenAI, NotEvaluated, PureAI, and Blended. This is useful in going to the specific chunk or code they are interested in.


4) All the above features are available on the Code review PR detail page as well, providing users with consistent user journeys.


5) You will also see a separate link on the left-hand-pane called the "Developer Group Beta". Upon clicking, the user will be taken to the Developer group page, where all information related to the developer group and their associated developers will be displayed. This page is going to evolve to provide more actionable insights. So, keep tuned.


6) Under the hood we made improvements in accuracy, including more varied human data in training set

Avatar of authorMatt Van Itallie