Category: Pipeline

I Hate Double Dippers, Yes I’m Talking About You Duplicate HTTP Poster

My team recently had an issue with a screen in an app allowing users to post a form multiple times. This results in all the posts being processed creating duplicate entries in the database. I didn’t dig into the solution with the team, but it reminded me of all the trouble this type of issue has caused me over the years. Now, I very much appreciate the circuit breaker pattern.

If you don’t have experience with implementing a circuit breaker, you can try a project like Polly.net if you’re using .NET.

http://www.thepollyproject.org/

In 2013 I wrote a post about ASP.NET Web Forms (scarey) where I felt the need to capture a hack to prevent double PostBack requests in the client with a circuit breaker. First, I wrote about debugging double PostBack issues. Then I posted a hack to short circuit the post backs. I had no mention of what motivated the post, I just had the sheer need and possibly panic knowing that codebase, to record these notes so I don’t have to figure it out again.

After reading it again, I wondered if I had the notion to force submit to return false only after the first click or if I found this on Google or StackOverflow? This looks like a nice quick trick that worked for me or I wouldn’t have posted it. I wonder if I was being creative, evolutionary, or a pirate (arrrr).

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I don’t know what a good practice for this is today, but I was shocked to see that I was digging so much to simplify this to a bullet list. I wonder if I ever better encapsulated the circuit breaker for this and I wondered what kind of production-issue-induced-anxiety-nightmares I was having.

Observable Resilience with Envoy and Hystrix works for .NET Teams

We had an interesting production issue where a service decided to stalk a Google API like a bad date and incured a mountain of charges. The issue made me ponder the inadequate observability and resilience we had in the system. We had resource monitoring through some simple Kubernetes dashboards, but I always wanted to have something more robust for observability. We also didn’t have a standard policy on timeouts, rate limiting, circuit breaking, bulk heading… resilience engineering. Then my mind wandered back to a video that I thought was amazing. The video was from the Netflix team and it altered my view on observability and system resilience.

I was hypnotized when Netflix released a view of the Netflix API Hystrix dashboard – https://www.youtube.com/watch?v=zWM7oAbVL4g. There is no sound in the video, but for some reason this dashboard was speaking loudly to me through the Matrix or something, because I wanted it badly. Like teenage me back in the day wanting a date with Janet Jackson bad meaning bad.

Netflix blogged about the dashboard here – https://medium.com/netflix-techblog/hystrix-dashboard-turbine-stream-aggregator-60985a2e51df. The simplicity of a circuit breaker monitoring dashboard blew me away. It had me dreaming of using the same type of monitoring to observe our software delivery process, marketing and sales programs, OKRs and our business in general. I saw more than microservices monitoring I saw system wide value stream monitoring (another topic that I spend too much time thinking about).

Unfortunately, when I learned about this Hystrix hotness I was under the impression that the dashboard required you to use Hystrix to instrument your code to send this telemetry to the dashboard. Being that Hystrix is Java based, I thought it was just another cool toy for the Java community that leaves me, .NET dev, out in the cold looking in on the party. Then I got my invitation.

I read where Envoy (on my circa 2018 cool things board and the most awesome K8s tool IMHO), was able to send telemetry to the Hytrix dashboard – https://blog.envoyproxy.io/best-of-all-worlds-monitoring-envoys-activity-using-hystrix-dashboard-9af1f52b8dca. This meant we, the .NET development community, could get similar visual indicators and faster issue discovery and recovery, like Netflix experienced, without the need to instrument code in any container workloads we have running in Kubernetes.

Install the Envoy sidecar, configure it on a pod, send sidecar metrics to Hystrix Dashboard and we have deep observability and a resilience boost without changing one line of .NET Core code. That may not be a good “getting started” explanation, but the point is, it isn’t a heavy lift to get the gist and be excited about this. I feel like if we had this on the system, we would have caught our Google API issue a lot sooner than we did and incurred less charges (even though Google is willing to give one-time forgiveness, thanks Google).

In hindsight, it is easy to identify how we failed with the Google API fiasco, umm.. my bad code. We’re a blameless team, but I can blame myself. I’d also argue that better observability into the system and improving resilience mechanisms has been a high priority of mine for this system. We haven’t been able to fully explore and operationalize system monitoring and alerts because of jumping through made up hoops to build unnecessary premature features. If we spent that precious time building out monitoring and alerts that let us know when request/response count has gone off the rails, if we implemented circuit breakers to prevent repeated requests when all we get in response are errors, if we were able to focus on scale and resilience instead of low priority vanity functionality, I think we’d have what we need to better operate in production (but this is also biased by hindsight). Real root cause – our poor product management and inability to raise the priority of observability and resilience.

Anyway, if you are going to scale in Kubernetes and are looking for a path to better observability and resilience, check out Envoy, Istio, Ambassador and Hystrix, it could change your production life. Hopefully, I will blog one day about how we use each of these.

Welcome to Simple Town, My Notes on ASP.NET Razor Pages

So, I took some time to finally look into Razor Pages and I was impressed and actually enjoyed the experience. Razor Pages simplify web development compared to MVC. Razor Pages reminds me of my failed attempts at MVP with Web Forms, but much less boilerplate. Feels like MVVM and still has the good parts of MVC. That’s because Razor Pages is MVC under the covers. I was able to immediately get some simple work done, unlike trying to get up and working with some of the JavaScript frameworks or even MVC for that matter.

Razor Pages provides a simplified abstraction on top of MVC. No bloated controllers, just bite sized modular pages that are paired with a page model (think Codebehind if you’ve used Web Forms). You don’t have to fuss over routing because routing defaults to your folder/page structure with simple conventions.

You may not want to use it for complex websites that need all the fancy smancy JavaScript interactivity, but simple CRUD apps are a great candidate for Razor Pages. IMHO I think I would select Razor Pages by default over MVC for server side HTML rendering of dynamic data bound websites (but I have very little experience with Razor Pages to stand behind that statement).

Here are some of my notes on RazorPages. This is not meant to teach RazorPages just a way to reinforce whats learned by just diving into it. These notes are the results of my research on questions that I ended up digging through docs.microsoft.com, StackOverflow and Google. Remember I’m still a RazorPage newbie so I may not have properly grasped some of this yet.

Page

A Page in Razor Pages is a cshtml file with the @page directive at the top of the page. Pages are basically content pages scripted as Razor templates. You have all the power of Razor and a simplified coding experience with Page Models. You can still use Razor layout pages to have a consistent master page template for your pages. You also get Razor partial pages that allow you to go super modular and build up your pages with reusable components (nice… thinking user controls keeping with my trip down Web Forms memory lane).

Page Models

Page Models are like Codebehinds from Web Forms because there is a one-to-one relationship between Page and PageModel. In fact, in the Page you bind the Page to the PageModel with the @model directive.

The PageModel is like an MVC Controller because it is an abstraction of a controller. It is unlike an MVC Controller because the Controller can be related to many Views and the PageModel has a beautiful, simplified, easy to understand one-to-one relationship with a Page.

Handlers

A PageModel is a simplified controller that you don’t have to worry about mapping routes to. You get to create methods that handle actions triggered by page requests. There is a well defined convention to map requests to handlers that I won’t go into because there are great sites that talk about the details of everything I bring up in my notes.

https://www.learnrazorpages.com is a great resource to start digging into the details.

BindProperty

BindingProperty is used when you want read-write two-way state binding between the PageModel and Page. Don’t get it twisted, Razor Pages is still stateless, but you have a way to easily bind state and pass state between the client and the server. Don’t worry, I know I keep saying Web Forms, but there is no View State, Sessions, or other nasties trying to force the stateless web to be stateful.

The BindingProperty is kind of like a communication channel between the Page and PageModel. The communication channel is not like a phone where communication can flow freely back and forth. Its more like a walkie talkie or CB radio where each side has to take turns clicking a button to talk where request and response are the button clicks. Simply place a BindingProperty attribute on a public property in the PageModel and the PageModel can send its current state to the Page and the Page can send its current state to the PageModel.

DIGRESS: As I dug into this I wondered if there was a way to do reactive one-way data flow like ReactJS. Maybe a BindingProperty that is immutable in the Page. The Page doesn’t update the BindingProperty when a BindingProperty is changed in the Page. Instead, when the Page wants to update a BindingProperty it would send some kind of change event to the PageModel. Then the PageModel handles the event by updating the BindingProperty which updates the Page state. We may need to use WebSockets, think SignalR, to provide an open communication channel to allow the free flow of change events and state changes.

What do you know, of course this has been done – https://www.codeproject.com/Articles/1254354/Turn-Your-Razor-Page-Into-Reactive-Form. Not sure if this is ready for prime time, but I loved the idea of reactive one way data flow when I started to learn about ReactJS. Maybe there is some real benefit that may encourage this to be built into Razor Pages.

ViewData

ViewData is the same ViewData we’ve been using in MVC. It is used to maintain read only Page state between postback (haven’t written “postback” since web forms… it all comes back around). ViewData is used in scenarios where one-way data flow from PageModel to the Page is acceptable. The page state saved to ViewData is passed from the PageModel to the Page.

ViewData is a data structure, a dictionary of objects with a string key. ViewData does not live beyond the request that it is returned to the Page in. When a new request is issued or a redirect occurs the state of ViewData is not maintained.

Since ViewData is weakly typed, values are stored as objects, the values have to be cast to a concrete type to be used. This also means that using ViewData you loose the benefits of Intellisense and compile-time checking. There are benefits that offset the shortcomings of weak typing. ViewData can be shared with a content Page’s layout and partials.

In a PageModel you can use the ViewData Attribute on public property of the PageModel. This makes the property available in ViewData. The property name becomes the key for the property values in the ViewData.

TempData

TempData is use used to send single-use read-only data from PageModel to the Page. The most common use of TempData is to provide user feedback after post actions that results in a redirect where you want to inform the user of the results of the post (“Hey, such and such was deleted like you asked.”).

TempData is marked for deletion after it is read from the request. There are Keep and Peek methods that can be used to look at the data without deleting it and a Remove method to delete it (I haven’t figured out a scenario where I want to use these yet).

TempData is contained in a dictionary of objects with a string key.

Razor Pages Life Cycle

Lastly, I wanted to understand the life cycle of Razor Pages and how I can plug-in to it to customize it for my purpose. Back to Web Forms again, I remember there being a well documented life cycle that let me shoot myself in the foot all the time. Below is the life cycle as I have pieced it together so far. I know we still have MVC under the hood so we still have the Middlewear pipeline, but I couldn’t find documentation on the life cycle with respect to Razor Pages specifically. Maybe I will walk through the source code one day or someone from the Razor Page team or someone else will do it for us (like https://docs.microsoft.com/en-us/aspnet/mvc/overview/getting-started/lifecycle-of-an-aspnet-mvc-5-application).

  1. A request is made to a URL.
  2. The URL is routed to a Page based on convention.
  3. The handler method in the IPageModel is selected based on convention.
  4. The OnPageHandlerSelcted IPageFilter and OnPageHandlerSelctedAsync IPageFilterAsync methods are ran.
  5. The PageModel properties and parameters are bound.
  6. The OnPageHandlerExecuting IPageFilter and OnPageHandlerExecutionAsync IPageFilterAsync methods are ran.
  7. The handler method is executed.
  8. The handler method returns a response.
  9. The OnPageHandlerExecuted IPageFilter method is ran.
  10. The Page is rendered (I need more research on how this happens in Razor, I mean we have the content, layout, and partial pages how are they rendered and stitched together?)

The Page Filters are cool because you have access the the HttpContext (request, response, headers, cookies…) so you can do some interesting things like global logging, messing with the headers, etc. They allow you to inject your custom logic into the lifecycle. They are kind of like Middlewear, but you have HttpContext (how cool is that?… very).

Conclusion

That’s all I got. I actually had fun. With all the complexity and various and ever changing frameworks in JavaScript client side web development, it was nice being back in simple town on the server sending rendered pages to the client.

Git Commit Log as CSV

Today, I needed to produce a CSV containing all commits made to a git repo 2 years ago. Did I say I hate audits? Luckily, it wasn’t that hard.

git log --after='2016-12-31' --before='2018-1-1' --pretty=format: '%h',%an,%ai,'%s' > log.csv

To give a quick breakdown:

  • git log – this is the command to output the commit log.
  • –after=’2016-12-31′ – this limits the results to commits after the date.
  • –before=’2018-1-1′ – this limits the results to commits before the date.
  • pretty=format:’%h’,%an,%ai,’%s’ – this is outputting the log in the specified format:
    • ‘%h’ – hash with surrounded by single quotes
    • %an – author name
    • %ai – ISO 8601 formatted date of commit
    • ‘%s’ – commit message surrounded by single quote.
  • > log.csv – output the log to a csv file named log.csv

I surround some values with single quotes to prevent Excel from interpreting the values as numbers or other value that loses the desired format. I had to look through the pretty format docs to find the placeholders to get the output I wanted.

It took a little digging through git docs to get here: https://git-scm.com/docs/git-log and https://git-scm.com/docs/pretty-formats. If I would have been smart and just searched for it I would have landed on this stack: https://stackoverflow.com/questions/10418056/how-do-i-generate-a-git-commit-log-for-the-last-month-and-export-it-as-csv.

Thinking About Microservices

We have been doing more and more work with containers and container orchestration in an effort to manage microservices. So, I have been thinking a lot about microservices and wanted to share some of my ramblings.

When I first heard about microservices I saw no real difference from service oriented architecture (SOA). Today, I still see no real difference, but thinking about microservices helps me see how to do SOA better.

Microservice has many definitions by many people and the meaning of the term is showing signs of standardizing. I still don’t subscribe to a particular definition, but when I think about microservices I have certain things that come to mind.

When I think of a microservice I think of a self-contained distributable versioned package. The package is where I put my custom assemblies and integrated dependent assemblies that I want to ship for consumption. The package contains my interface (UI, API), service, and infrastructure including external services and internal private persistence.

Internal private persistence and state could be something that separates SOA from microservice. Isolating state is one way that this architecture helps limit the bad side effects that come up with shared databases in SOA. Building a system based on microservices is kind of like building a thread safe application. The difference is instead of being able to distribute across threads I want to distribute across the network. Just like isolated state is important in thread safety isolated state (persistence) is important in microservices. There is so much more that can be said on this one point, but I won’t. Just know that isolating state is something I think about and I am still learning about how to deal with this.

Continuing my microservice thoughts, the microservice package has a well defined backwards compatible contract for the service interfaces that provides accessibility to a responsive, focused and cohesive bounded context for a specific domain. The package optionally has the microservice configurations, logs, traces, tests, documentation, SDK, and common services like pulling or pushing logs to a central log server or east-west message based communication with other microservices (e.g. Actor System). This east-west service-to-service communication is a large topic when you bring monolithic relational normalized database thinking to microservices. I still sometimes incorrectly think of how to reduce redundancy across microservices and provide deep relations and dependencies between the service like I would in a monolith. So, thinking about microservice is also retraining my thought process, it’s a paradigm shift.

These microservice distributions are all-in-one neat little packages of independent composable component goodness. I can build a microservice place it in a container and independently ship it through an automated delivery pipeline, continuously monitor it, elastically scale it, deliver the service in a highly available manner that is recoverable from internal failure and still responsive when there are failures in dependent services.

My favorite aspect of a microservice is that it is easy to reason about and maintain. My least favorite aspect of microservice architecture is that it is difficult to reason about and maintain. Microservices are simple because they are small and self contained. They are difficult because when composed together to form a system they constitute a distributed system and distributed systems have been a thorn in the side of my development career. In the end, I still love microservices because there are many new strategies and frameworks to lessen the pain of distributed systems that make building these systems very interesting to me.

An Agile Transformation

I wrote this a few years ago, but I’m going through a similar agile transformation right now. Although, every agile transformation is different, this still makes sense to me although it is just a draft post. I figured I’d just post it because I never search my drafts for nuggets of knowledge :).

If we are going to do Kanban we shouldn’t waste time formally planning sprints. Just like we don’t want to do huge up front specifications because of waste cased by unknowns, we don’t want to spend time planning a sprint because the work being done in the sprint can change anytime the customer wants to reprioritize. We should have a backlog of prioritized features. The backlog is regularly prioritized (daily, weekly…) to keep features available to work. If we want to deliver a specific set of features or features in two weeks, prioritize them and the team will do those features next.

There is a limit on the number of features the team can have in progress (work in progress or WIP). Features are considered WIP until they pass UAT. Production would be a better target, but saying a feature is WIP until production is a little far fetched if you aren’t practicing continuous delivery. So, for our system production is considered passing UAT. When the team is under their WIP limit they are free to pull the next feature from highest priority features in the backlog.

This is going to most likely reduce resource utilization, but will increase throughput and improve quality. Managers may take issue at developers not being used at full capacity, but there is a reason for this madness and hopefully I can explain it.

Having features pulled into the pipeline from a prioritized backlog instead of a planned sprint allows decisions on what features to be worked to be deferred until the last possible moment. This provides more agility in the flow of work in the pipeline and the product owner is able to respond quickly to optimize the product in production. Isn’t agile what we’re going for? Pulling work with WIP limits also gives greater risk management. Since batch sizes are smaller, problems will only affect a limited amount of work in progress and risk can be mitigated as new work is introduced in the pipeline. Focusing on a limited amount of work improves the speed at which work is done. There is no context switching and there is a single focus on moving a limited amount work through the system at one time. This increases the flow of work even though there may be times when a developer is idle. The truth is the system can only flow as fast as its slowest link, the constraint. Having one part of the system run at full capacity and overload the constraint introduces a lot of potential waste in the system.

On my current team, we have constraints that determine how quickly we can turn around a feature. Currently, code review and QA are constraints. QA is the largest constraint that limits faster deployment cycles, but more on that later. If we follow the five basic steps outline in the TOC from the book The Goal, we would:

  1. Identify the constraint(s) – in this instance it’s code review and manual testing
  2. Exploit the constraint to maximize productivity –
  3. Subordinate all other steps or processes to speed up or reduce capacity of the constraint – no new work may enter as WIP until the constraint has WIP available.
  4. Elevate the constraint – for us we will prioritize work that helps remove to remove these work centers as constraints.
  5. Repeat

The plan is to have developers do code reviews any time WIP stops the movement of work. Also, developers should create automated tests to help lessen the work that QA has to do. The reason we don’t first focus on optimizing QA processes is because focusing on simply optimizing QA processes would actually increases the capacity for QA without increasing the speed at which we can flow work to production. We don’t want to increase the number of features that QA can handle. We also don’t want to speed up manual testing because it is important to take the proper time in testing. What we want to do is remove manual regression as work for QA to open us up to deliver new features to QA faster and get QA to deliver the feature to production faster. QA can focus on what they do best, test. Not running mundane scripted checks.

Normally, we would have to wait for a manual regression test cycle to occur and couldn’t introduce new work because it would invalidate the regression test. With automation handling +80% of regression QA can move faster, actually test more, and we can not only increase throughput through the entire system, but the overall quality of the product.

Monitoring Delivery Pipeline

We track work through the delivery pipeline as features. A feature in this sense is any change, new function, change existing function, or to fix a defect. Features are requested on features kept in a central database. We monitor the delivery pipeline by measuring:

  • Inventory
  • Lead Time
  • Quantity: Unit of Production
  • Production Rate

Inventory

Inventory (V) is any work that has not been delivered to the customer. This counts all work from the backlog to a release awaiting production deployment. Whenever there is undelivered work that is considered invalid it becomes an Operational Expense. Invalid meaning it won’t be delivered at all or there are issues like defect or doesn’t match spec. Invalid work is wasted effort and in the case of a defect causes expensive un-budgeted rework. In traditional cost accounting inventory is seen as an asset, but in TOC it is a “potential” Operational Expense if it is not eventually delivered to customer so turning inventory as fast as possible without injecting defects is a goal.

Quantity: Unit of Production

Quantity: Unit of Production (Q) is the total number of units of work (feature) that have moved through our delivery pipeline to date. Our unit of production is a feature. When a feature is ready to be deployed to production we can increase Q one unit, but the feature is still considered inventory until it has been delivered to customer. If a customer decides they don’t want the feature or some other reason to stop the deployment of the feature, it is counted as an Operational Expense and Q is reduced one unit.

Lead Time

Lead time (LT) is the time it takes to move a feature, one Q, from submission to the backlog to deployed to a customer in production.

Production Rate

Production rate (PR) is the number of Q delivered during a time period. 3 features per month, 2 features per week…

Optimize Delivery Pipeline for Lead Time

We should strive to optimize the delivery pipeline for lead time instead of production rate. The Theory Of Constraints – Productivity Metrics in Software Development posted on lostechies.com explains this well.

Let’s say our current lead time (LT) is 1 unit (Q) in a week or a production rate (PR) of 4 Q per month. If we optimize LT to 1 Q in 3 days, we will see a jump in PR to 6.67 Q per month or a 59% increase.

If we focus on optimizing PR, we may still see improvement in LT, but it can also lead to only an increase in inventory. The PR optimization may increase Q that is undeliverable because of some bottleneck in our system so the Q sits as inventory. The longer a feature sits in inventory the more it costs to move it through the pipeline and address any issues found in later stages of the pipeline.

So, to make sure we are optimizing for LT we focus on reducing waste or inventory in the pipeline. The delivery team keeps a single purposed focused on a limited amount of work in progress to deliver what the customer needs right now, based on priority in the backlog. Reducing inventory reduces Operation Expense. (Excuse me if I am allowing some lean thinking into this TOC explanation)

Metrics

Investment

Investment (I) is the total cost invested in the pipeline. In our case we will count this as hours invested.

Operating Expense

Operating expense (OE) is the cost of taking an idea and developing it to a deliverable. Any fixed overhead is considered OE. We will just use salaries of not only developers, but BA, QA, IT as our OE. Not sure how we will divide up our fixed salaries (still learning).

Throughput

Throughput (T) is the amount earned per Q. It is calculated by taking the amount of features delivered to production minus the cost of delivering the feature.

Throughput Accounting

To maximize ROI and net profit (NP) we need to increase T while decreasing I and OE.

NP = (T – OE)

ROI = NP/ I

Average Cost Per Feature

Average cost per feature (ACPF) is the average amount spent in the pipeline to create a feature.

ACPF = OE/Q

There are more metrics that we can gather, monitor, and analyze; but we will keep it simple for now and learn to crawl first.

Average Lead Time Per Feature

The average time it takes to move a feature from the backlog to production. We also calculate the standard deviation to get a sense on how varying work sizes in the pipeline affects lead time.

Bonus: Estimating Becomes Easier

When we begin to monitor our pipeline with these metrics estimating becomes simpler. Instead of estimating based on time we switch to estimating based on size of feature. Since we are tracking work, we have a history to base our future size estimates on.

 

Issues in Transformation

Our current Q is a release, a group of features that have been grouped together for a deployment. We will build up an inventory of features over a month at times before they are delivered to production. This causes an increase in inventory. It would be better to use a feature instead of a release as our Q. When a feature is ready, deliver it. This reduces inventory and increase the speed at which we get feedback.

To change our unit, Q, to feature we have to attack our largest constraint, QA. Currently, we have to sit on features or build up inventory to get enough to justify a QA test cycle. We don’t want to force a two week regression on one feature that took a couple days to complete. So, reducing the test cycle is paramount with this approach.

References

The Goal: A Process of Ongoing Improvement, by Eliyahu M. Goldratt

The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win, by Gene Kim, Kevin Behr, and George Spafford.

The Metrics in TOC: Productivity Metrics In Software Development, by Derick Bailey, https://lostechies.com/wp-content/uploads/2011/04/TheoryOfConstraints-ProductivityMetricsInSoftwareDevelopment.pdf

Agile Management for Software Engineering, by David J. Anderson

Reaching The Goal, by John Arthur Ricketts

Applying Theory of Constraints to Manage Bottlenecks, by Kamran Khan, http://www.isixsigma.com/methodology/theory-of-constraints/applying-theory-constraints-manage-bottlenecks/

http://chronologist.com/blog/2012-07-27/theory-of-constraints-and-software-engineering/

http://chronologist.com/blog/2012-10-04/buffer-management-and-risk-management-in-TOC/

https://www.timecockpit.com/blog/2013/08/30/Project-Reporting-in-Agile-Projects

 

 

 

Adding Report to Existing TFS 2017 Project

I had an issue where I couldn’t see reports for my TFS projects because they weren’t installed. I knew this because I opened SQL Reporting Services and I didn’t see a folder for my project under the TFS collection’s folder. I did a little digging and found a command that I could run to install the reports:

  1. Open administrator command prompt on server hosting TFS.
  2. Change directory to C:\Program Files\Microsoft Team Foundation Server 15.0\Tools
    Note: 64bit would be Program Files (x86)
  3. Run TFSConfig command to add project reports

TFSConfig addprojectreports /collection:”https://{TFSServerName}/{TFSCollectionName}” /teamproject:{TFSProjectName} /template:”Scrum”

You should replace the tokens with names that fit your context (remove the brackets). The template will be the template for your project:

  • Scrum – you will have backlog items under features
  • Agile – you will have stories under features

There’s another one, CMMI, but I’ve never used it. You should see a requirements work item, but I’m not sure if this template has a feature item.

Once you run the command, the reports will be added and you will be able to see how your team is doing by viewing the reports in SQL Reporting Services.