Skip to main content

Visual Studio 2012 - Strange behavior durring the commit

Based on TFS and local machine configuration, when we make a check-in we can set a task associate with the changeset to Resolve status. There are times when you cannot change a task status to Resolve, for example different policy on TFS machine. If you are using Visual Studio 2008 or Visual Studio 2010, everything will be away – an error message will be displayed that notified you that the commit ended without success.
When working with Visual Studio 2012 you will discover that even if an error is displayed during the check-in, the changeset will be pushed to the source control without having a task associate to it. You will receive the TF237124 error, but only after the commit is pushed to source control. This will happen even if on the TFS you enforce a rule that allow to commit only if a task is specified.
I hate this behavior, because a commit is made to source control even if an error appears.
To be able to assign a task to the changeset, you will need to open the task in TFS or in the browser, navigate to ALL LINKS tabs and link the changeset to the task (you will need to know the changeset number).
If you want to change the default behavior and not to mark the task as resolve in the TFS, you will need to set to the false value the following key:
// Visual Studio 2010
[HKEY_CURRENT_USER\Software\Microsoft\VisualStudio\10.0\TeamFoundation\SourceControl\Behavior]
"ResolveAsDefaultCheckinAction"="False"
//Visual Studio 2012
[HKEY_CURRENT_USER\Software\Microsoft\VisualStudio\11.0\TeamFoundation\SourceControl\Behavior]
"ResolveAsDefaultCheckinAction"="False"
The problem with a check-in that is made even if an errors appears I expect to be solved in the near future. Is not acceptable this behavior.

Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP