Azure OpenAI GPT model to review Pull Requests for Azure DevOps
In recent months, the use of Generative Pre-trained Transformer (GPT) models for natural language processing (NLP) has gained significant traction. GPT models, which are based on the Transformer architecture, can generate text from arbitrary sources of input data and can be trained to identify errors and detect anomalies in text. As such, GPT models are increasingly being used for a variety of applications, ranging from natural language understanding to text summarization and question-answering. In the software development world, developers use pull requests to submit proposed changes to a codebase. However, reviews by other developers can sometimes take a long time and not accurate, and in some cases, these reviews can introduce new bugs and issues. In order to reduce this risk, During my research I found the integration of GPT models is possible and we can add Azure OpenAI service as pull request reviewers for Azure Pipelines service. The GPT models are trained on developer codebases and are able to detect potential coding issues such as typos, syntax errors, style inconsistencies and code smells. In addition, they can also assess code structure and suggest improvements to the overall code quality. Once the GPT models have been trained, they can be integrated into the Azure Pipelines service so that they can automatically review pull requests and provide feedback. This helps to reduce the time taken for code reviews, as well as reduce the likelihood of introducing bugs and issues.Setup Azure DevOps Self Hosted Agent On-Premise & Troubleshooting Guidelines
Azure DevOps supports the deployment of the builds from release pipelines to the on-premise environments using self-hosted pipeline agents. This article describes creating an Azure DevOps self-hosted agent in an on-premise Windows machine and some standard troubleshooting steps in case any issue.Azure DevOps Pipelines: Templates Tasks and Jobs
Fully leveraging the power of Azure DevOps (ADO) one should evaluate how to best use templates. This post focuses on using templates for tasks and jobs. Previous posts on Azure DevOps Pipelines: Environments and Variables (microsoft.com) and Azure DevOps Pipelines: Tasks, Jobs, Stages (microsoft.com)Azure DevOps Pipelines: Practices for Scaling Templates
When starting to leverage YAML Pipelines at scale you are most likely going to gravitate towards a solution that leverages a consolidated template repository. Since we will have multiple pipelines calling the same repository it's important we get guidance on how to start and structure this new repository, so it scales with our organization and their pipeline adoption.Getting secrets from Key Vault in YAML pipeline
If you have ever created an Azure App Service or Azure Function App that uses app settings, then you have dealt with the problem of how you are going to get those settings secure and updated correctly in each environment. You need a secure location to store this information and then be able to access it during your deployment process. Azure Key Vault and using the Azure Key Vault task inside a deployment pipeline in Azure DevOps can solve this problem for you. If you prefer video, then have a look at this as it will walk you through the steps of getting this setup.
