Work smart, then hard

October 4, 2020 @ 10:12 am Posted to .Net by Antony Koch

Often, I hear friends and colleagues discuss their daily efforts in one of two ways. Either they say hard work gets you to where you want to be, gets the job done, or they tell me to work smart, not hard.

While reflecting on my career and approach, I realised it’s a mix of the two that gets the job done.

Every project I’ve worked on serves as an example of this, eventually. Early hubris, caused by overconfidence in how smart we’re working, typically results in hard work at the project’s end. We didn’t reflect enough during the process to ascertain whether we were still working smart, or if, in fact, we had been working dumb the entire time.

To work smart means to spend as much time as possible planning the shortest route to our short term goal, without compromising our long term goal. In software terms, it means how can we write as little code at possible that both achieves our goal and provides the greatest value towards the codebase and its users while accruing as little technical debt as possible.

To work hard means that once we’ve brainstormed and agreed on how to work smart, we set an ambitious goal requiring hard work from the entire team in order to achieve it.

We then agree regular checkpoints to switch back into smart mode, introspectively validating whether we had worked smart, are still working smart, or need to down tools and completely re-evaluate.

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Learning about more than code

June 7, 2019 @ 8:59 pm Posted to .Net by Antony Koch

Not every role is an education or exercise in improving ones development abilities. Not every client offers a rich domain in which one can exercise one’s desire to further understand DDD and its associate concepts. But each role does have something to offer if you’re prepared to look in other places.

With my current client, I’ve learned a lot about getting things delivered rather than building something perfect. I’ve finally learned how important documentation can be, not least when it comes to buying yourself more time for delivering value, because you’re not explaining the same thing to person 30, you’re sending them a link. And I’ve truly seen and understood the value of being a mentor and encouraging constant self improvement.

I’ve also had reinforced my opinion that processes and rules only serve as obstacles. That ceremonies should be discarded if they are not adding value. That trusting one’s team to get the job done trumps any other metric or heuristic. Do something that works, then reflect and see if we might make it better. But – and this is a big but – only in discussion with the team. Only with complete buy in from the team. We can agree that we all disagree and try something for a week, then reflect, adapt, and go again.

I have also seen how one person’s desire to continuously improve themselves and those around them can reap huge rewards. Now that person is leaving, I hope to take up the mantle of improving not only me, but all those around me, with regular katas, and a better, deeper engagement with juniors and mids.

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Leaky Abstractions

October 23, 2018 @ 8:56 am Posted to .Net by Antony Koch

As a software engineer, it’s important to understand what leaky abstractions are, how to spot them, and how to mitigate against them. The term often remains unused by teams, most likely due to gaps in knowledge or training, or for fear of causing offence to colleagues. Understanding its core concepts will help you become a better developer, allowing you to spot potentially poisonous changes that may wreak havoc on your systems in the not-too-distant future. So what does the term mean? How can we spot it? What are some good examples? And when we do spot it in a codebase or architecture diagram, what mitigating actions can we take?

Put simply, a leaky abstraction is any API in your codebase or architecture that reveals too much information about its underlying implementation. One example most of us have seen is an API that returns its fields in all upper case letters, each of which matches the underlying database’s column name. Or an API whose functions and parameters match precisely those of its underlying implementation. Good API design – and by API I mean any interface we use to communicate with a software program, not just an API in the sense of a RESTful service – shields us from the information we don’t need to know. It asks only for that which it needs, and obfuscates unsightly legacy software that might ultimately be serving up its functionality.

When implemented correctly, an abstraction holds up against any and all use cases you can throw against it. If you’ve missed something, it is easier than not to plug in. Writing software to the right level of abstraction, avoiding leaks, is our toughest challenge as developers, but it also offers the greatest rewards. Developers love to be correct, and writing code or a design that holds up to any and all scrutiny feels great. But more than that, it proves that we have understood the problem and have created a solid solution that tackles our problem well enough for now.

An example of a leaky abstraction comes from my current contract, which was a leaky abstraction introduced by a third party into one of the solutions I was working on. We had held meetings regarding service contracts, and had proposed a minimal contract consisting of an entity type and id. . Consequently, the HTTP API would need to do some fetching of resources under the covers, but in doing so it would shield the outside world from needing to know more than the bare minimum. This allowed existing systems in the architecture to consume this service without any need to retrieve additional information in order to call the new API. Unfortunately, the third party didn’t grasp this concept, and decided to introduce several additional fields. Now, it’s at this point I should mention that this was an API over a well know big-name document management platform, the client’s system-of-choice for storing documents. The structure in which the documents were held involved some client metadata, some subsystem data from the client’s CRM system of choice, and one or two other friendly-names from said system. These references, metadata and IDs were now all required as part of the API contract. The rationale was that the API shouldn’t need to fetch additional data from the CRM system ‘because it would be too slow.’ This sounded alarm bells in me. A classic leaky abstraction: The document management system’s underlying file system was bleeding profusely out of their API. I set into motion trying to explain this to the third party. The issue was that any consumer of this API now needed to know a hell of a lot more than it ought to. Want to upload one attachment to a note on a back office incident? You now need to fetch customer details along with the parent hierarchy of IDs relating to the note you’re administering. These changes would permeate throughout the system. I tried to explain that the ‘slow’ call made in the API would now be spread into tens, if not hundreds, of calls from outlying systems that need to fetch the aforementioned data, however the third party – and unfortunately the client – simply did not get the concept. I had failed to appropriately explain the issue and it’s potential for cost. Several weeks later we wound up specifying a new piece of the solution which would cost the client a lot of money to implement, all because the leaky abstraction had not been plugged.

So how can you spot a leaky abstraction? Start with an empty API, and introduce a method or endpoint that has a requirement. Now what’s the smallest amount of information you can introduce that allows you to achieve the APIs goal? Make sure you understand whose job it is to know what information. In the above example, whose job is it to know the underlying folder structure of your document management platform? Is it a website that allows users to add notes to incidents, or the API whose job it is to shield users from such mundanity? Consider why you are building this API in the first place. You are attempting to build something of value; a streamlined way to access a capability. The most streamlined way possible is a clear, concise, and expressive API, with an ability to perform small, discrete tasks to manipulate its internal state while shielding the outside world from how you are manipulating that state.

Perhaps the best question we can ask, again using the example above, is how our API contract look were we to move away from the current document management platform? If we can say that our concepts and naming conventions stand up well, then we have a solid abstraction for document storage within our domain. If we realise we have parameters called parent folder or vnd_doc_sp_search_id, then we have a leaky abstraction.

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F# on aspnetcore: Escaping the framework

February 15, 2017 @ 2:24 pm Posted to .Net, dotnetcore, F# by Antony Koch

Mark Seemann has both blogged and talked about escaping the OO .Net Web API framework in order to use a more idiomatic functional style. This is achieved by providing a function per verb to the controller’s constructor, and replacing the IHttpControllerActivator:

type CompositionRoot() =  
    interface IHttpControllerActivator with
        member this.Create(request, controllerDescriptor, controllerType) =
            if controllerType = typeof<HomeController> then
                new HomeController() :> IHttpController
            elif controllerType = typeof<DoesSomethingController> then
                let imp x = x * x
                let c = new DoesSomethingController(imp) :> _
                <| ArgumentException(
                    sprintf "Unknown controller type requested: %O" controllerType,

Then in the startup for your app (global or Startup):


This works great, and I love its honesty. It makes you feel the pain, to quote Greg Young, and in composing tight workflows in your composition root the ‘what’ of your domain is laid bare.

However, this won’t work in aspnetcore because it’s more Mvc and less WebApi, or – to use MS phrasology – more Web and less Http, meaning there’s no IHttpControllerActivator. The fix is simple, and aligned with the terminology: drop the ‘http!’ One instead replaces the IHttpControlleractivator with an IControllerActivator instance inside the aspnetcore DI framework and the same results are achieved:

type CustomControllerActivator() =  
    interface IControllerActivator with
        member this.Create(c : ControllerContext) : obj =
            if c.ActionDescriptor.ControllerTypeInfo.AsType() = 
typeof<DoesSomethingController> then  
                let imp x = x * x
                new DoesSomethingController(imp) |> box
                invalidArg "controllerType" "Cannot find controller"

        member this.Release (c : ControllerContext, ctrl : obj) =   

And in your OWIN startup:

    member this.ConfigureServices (services:IServiceCollection) =
        services.AddSingleton<IControllerActivator>(new CustomControllerActivator()) |> ignore

        services.AddMvc() |> ignore


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A non-generic AutoFixture Create method

October 24, 2016 @ 8:03 am Posted to .Net by Antony Koch

Sometimes I need to dynamically generate test fixtures, but don’t in the test context have the ability to use generics, instead having only an instance of a Type.

I looked through the AutoFixture code and managed to find a reflection-friendly way to return me an object that can then be changed using Convert.ChangeType where necessary. Here’s the snippet:

typeof(SpecimenFactory).GetMethods().Single(x => x.IsStatic && x.IsGenericMethod && x.Name == "Create" && x.GetParameters().Length == 1 && x.GetParameters().Single().ParameterType == typeof(ISpecimenBuilder)).MakeGenericMethod(type).Invoke(fixture, new [] { fixture });

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Gauge your code’s adherence to the single responsibility principle

September 26, 2016 @ 7:40 pm Posted to .Net, OO by Antony Koch

During a routine ponderance on software engineering, I was thinking about a conversation recently in which we discussed how and when to copy and paste code. The team agreed that it’s OK to copy code once or twice, and to consider refactoring on the third occasion. This led me to wonder about the size of the code we copy, and how it might indicate adherence to the single responsibility principle.

For the uninitiated reader, the single responsibility principle – one of the first five principles of Object Oriented Programming and Design – can be succinctly summed up as:

“A class should have one reason to change”

The subject of many an interview question, often recited as rote, yet often misunderstood, the core premise can – I think – be understood by just talking about the code block, or class, at hand, in the form of a response to the question: “Tell me all the reasons you might need to edit this class?” Responses such as:

  • “If we want to change the backing store, we have to change this class.”
  • “If we want to change the business rules for persisting, we have to change this class.”
  • “If we want to change the fields used in the response, we have to change this class.”

When recited in one’s mind, are all that are required. And by considering the answers carefully, we can make an informed decision about whether to refactor, or that we’re actually happy with what we have, and are left with the option to change the class easily at a later date. The latter part of this sentence is critical to becoming a better developer, because what we have might be acceptably incomplete, and refactoring might take an inordinate amount of time and fail to offer significant business value to justify the expense.

This said, is copying and pasting code OK? Mark Seemann wrote an excellent blog post on the subject – which I won’t attempt to better – suffice to say I agree, and that it’s OK to copy and paste under a certain set of circumstances. The primary concern is the tradeoff: to suitably generify code requires at most a deeper understanding of the abstractions in play, and at least the ability to introduce dependencies between classes and modules that might not have otherwise been required. A quick copy paste of code that’s unlikely to change is not going to kill anyone. It might introduce an overhead should the code’s underlying understanding change, however volatile concepts do not in the first place represent good candidates for copying and pasting.

Now to wistfully return to the subject at hand – how can we use copying and pasting to judge our code’s adherence to the single responsibility principle? Quite simply: if we can copy only a line or two, then the surrounding code within the method body is perhaps not doing as targeted a job as we might hope. If we can copy entire classes, we can say that we’ve adhered strictly to the core tenets of the single responsibility principle: this class has such a defined person it can be lifted and shifted around the codebase with ease.

This means we can judge any of our code in a couple of ways: answer the question “what reasons does this class have to change?” as well as being honest with ourselves about our ability to copy and paste this code into a different codebase without being refactored.  Would half of the class be thrown away? Would we have to change a bunch of code in order to fit a different persistence model, say copying from a SQL Server backed system to a system backed by Event Store? I think it’s an interesting idea, and definitely one I’m going to keep trying in the coming days.

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A simple Dapper Wrapper

August 5, 2016 @ 10:28 am Posted to .Net by Antony Koch

If you need to sub out some Dapper functionality, and aren’t too worried about the specifics of the call, then I’ve crafted a nifty class you can use to perform just such a task.

In all it’s glory, the subbable IDapperWrapper, with its default implementation:

    public interface IDapperWrapper
        IEnumerable<T> Return<T>(IDbConnection connection, Func<IDbConnection, IEnumerable<T>> toRun);
        T Return<T>(IDbConnection connection, Func<IDbConnection, T> toRun);
        void Void(IDbConnection connection, Action<IDbConnection> toRun);

    public class DapperWrapper : IDapperWrapper
        public IEnumerable<T> Return<T>(IDbConnection connection, Func<IDbConnection, IEnumerable<T>> toRun)
            return toRun(connection);

        public T Return<T>(IDbConnection connection, Func<IDbConnection, T> toRun)
            return toRun(connection);

        public void Void(IDbConnection connection, Action<IDbConnection> toRun)

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How can one ‘keep it simple’ in a complex system?

June 5, 2016 @ 8:56 pm Posted to .Net, Tech by Antony Koch

The often used phrase “Keep it simple, stupid,” abbreviated KISS, is solid advice in the field software development. We strive for simplicity, planning and refactoring continuously to ensure our code is extensible, reusable, and all those other words ending in ‘ble’ that apply. But what is simple? And how can things be kept simple in a complex system with several complex domains?

Simple is subjective. To some it means few moving parts, to others it means code that reads like a book, even if it’s repeated in several places. Disagreements can easily arise when deciding what simple means. Keeping an open mind is critical with regards to the definition of simplicity, because all sides can have valid arguments.

Complexity, like simplicity, is also subjective. Code can be composed in a complex way, however the component parts may be implemented simply. So can only simple things be over complicated?

There’s a saying: work smart, not hard, that applies to software development more deeply than in many other domains. Finding smart solutions usually means less code, fewer moving parts and a more concept-based approach.

To some developers a concept-based approach can be perplexing. Simplicity masquerading as complexity that remains obscure until scrutinised further. It can also, however, be over-engineered — six classes used where one might have sufficed until a later date.

Does this mean those developers who find smart solutions complex aren’t up to scratch, or should the codebase cater to the needs of the team and be legible to all? In my opinion, no. Smart trumps legibility every time, for the simple reason that legibility is subjective and based on the abilities of the reader. Some are baffled by lambdas and some aren’t. This doesn’t mean teams should avoid lambdas, it means teams should shift dead weight.

All of this begs the question: can something that seems complex always be reduced to something simple? In most cases, yes. A video I watched (which I will need to find later as the author escapes me) stated that in most cases he could walk into a company and reduce a code base by a factor of 80%. That is to say that a 100,000 line codebase could be reduced to roughly 20,000 lines of code.

Part of the reason this is, in my eyes, true, is because teams wilfully introduce technical debt, qualifying its introduction with ‘We’ll fix it if we need to later’. This is a flag to me that says “we know we aren’t doing it properly.” This is not counterintuitive to Ayende’s JFHCI — in fact it works with it: work smart, not hard. His example of hard coding is not an introduction of technical debt, it’s a forward thinking solution with minimal down payment now.

So how do we keep things simple in complex domains? Here’s a bulleted list of how it can be done:

Limit your abstractions

Don’t introduce a phony abstraction in order to make it mockable. If you see an IFoo with a single implementation Foo, you’re overcomplicating and you’re missing the point of interfaces in the first place. Code should be written to concepts as per Ayende’s limit your abstractions post.

Test outside in

Test your components using their public API. Don’t test the components internals because you’re then testing implementation. This allows two benefits:

  • Get the internals working correctly, quickly, with minimal fuss and with good test coverage
  • Once complete, it allows you to refactor into any concepts you may have uncovered along the way.

Work smart, not hard

Highly focussed components with specific jobs connected in a smart way. Some people might not understand them; it’s your job to enlighten them. If they still don’t understand it, cut them loose and hire someone who does. The inverse is true too, though — if everyone disagrees with your code it’s either wrong and you need to learn, or it’s right and you need to leave.

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A quick and dirty XUnit/AutoFixture MongoDb test harness

September 21, 2015 @ 6:37 am Posted to .Net, Testing by Antony Koch

As part of my new journey into outside-in testing I’ve – of course, for those that know me – looked into the tooling aspect. A part of that is implementing a MongoDB test harness injected via AutoFixture’s XUnit plugins.

As a quick side note, I think tooling is critical to elevating ones self from being a good developer to becoming a great one, as it removes the need to focus on anything other than writing production code. An example of this is my usage of NCrunch for continuous test execution. I can’t extol the benefits of not stopping to run all my tests enough, and it’s ben great to see the ongoing development of NCrunch since it’s free days.

Anyway – back to the point at hand.

I am building a MongoDb-backed application outside of my regular 9-5 engagement with my banking client, Investec, and needed a quick way to access the db in my tests. Avoiding ceremony was critical as I’m really into working on the production code as much as possible, not wasting time writing tests that will need to be rewritten when my implementation invariably changes as I learn more about the system I’m building. My app involves a twitter login, meaning everything I do from a database perspective is best served using the twitter UserId field. For cut one, I’ve come up with the following code, which I’ve added comments to for clarity:

using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using System.Web.Configuration;

using MongoDB.Bson;
using MongoDB.Driver;

namespace Amko.TwendingToday.Fast.Mongo
    public class MongoDbHarness : IDbHarness
        private IMongoDatabase _db;

        public MongoDbHarness()
            // Extract connection string from config
            var connectionString = ConfigurationManager.AppSettings[ServiceConstants.MongoDbUri];

            // Get a thread-safe client object by using a connection string
            var mongoClient = new MongoClient(connectionString);

            // Get a reference to a server object from the Mongo client object
            const string databaseName = "twitterapp"; // I've taken the real name out here to avoid giving spoilers :)

            // bootstrap our DB object
            _db = mongoClient.GetDatabase(databaseName);


        public async Task<BsonDocument> GetAsync(string collection, string userId)
            // Extract collection as a set of BsonDocument. The production code deals in
            // concrete domain objects, but for test purposes I'm loathe to spend time
            // building and rebuilding the ever changing domain in my test code. It
            // doesn't offer much over string indexes
            var coll = _db.GetCollection<BsonDocument>(collection);

            // build a filter for the get
            var filterDefinition = Builders<BsonDocument>.Filter.Eq("UserId", userId);

            // pull out the matching docs asynchronously
            var list = await coll.Find(filterDefinition).ToListAsync();

            // return the first one
            return list.FirstOrDefault();

    public interface IDbHarness
        Task<BsonDocument> GetAsync(string collection, string userId);

Although I’ve mentioned it in the above comments, I think it’s worth mentioning the point regarding the test domain and my decision not to create it. It’s common to build a copy of the domain object in test code to deserialise database into, or in worse cases to use the production models, but I decided against this. I feel that for it to be a true outside in test I should express my query against the resultant JSON from the DB as I might query the JSON in the real world, as it’s an extra verification step against how I think the system is working.

The injection of this is handled by AutoFixture using the following specimen builder:

    public class MongoHarnessSpecimenBuilder : ISpecimenBuilder
        public object Create(object request, ISpecimenContext context)
            var type = request as Type;

            if (type == null || type.IsPrimitive)
                return new NoSpecimen(request);

            if (type == typeof(IDbHarness))
                return new MongoDbHarness();

            return new NoSpecimen(request);

And this allows me to build my tests like this:

        public void TracksUserWhenTheyLogin(
            AuthController sut, 
            ActionResult actionResult,
            AuthenticatedUser user,
            IDbHarness dbHarness,
            DateTime now,
            string returnUrl)

I’ll dig out some resources for building the AutoFixture framework backing my AutomapData attribute and post them here later.

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What can we learn by refactoring without touching our unit tests? A converts’ explanation of outside-in testing.

August 26, 2015 @ 8:13 pm Posted to .Net, Mocking, Testing, Unit Testing by Antony Koch

Everywhere I’ve worked develops ‘features’. No great revelation there. However a feature is often, or at least ought to be, quite a small piece of functionality. A stripe of business value carved out of a larger solution. This stripe can be tested and released within a sprint while adding value to the business.

A lot of features can be expressed using a ‘happy path’: that is to say that there tends to be very few ways in which this feature can be executed without any exceptions thrown or any downstream entities not being found.

A typical scenario expressed in Gherkin takes the following form:

Given a context
When an action is perform
Then there is an outcome

Expanding upon this with a more real world set of scenarios:

Given an Amazon Prime customer
When the customer searches for goods
Then a Prime delivery option should be displayed

Given a non Amazon Prime customer
When the customer searches for goods
Then no prime delivery option should be displayed

Simple enough, right? Those 6 lines of text define everything we’re about to code up.

So of the time spent coding up, how much of it, in a TDD setting, is spent on unit tests? 50%? 75%? 75% of your time spent writing code that no user will touch.

Now let’s say the next feature comes along and we learn that our initial solution requires some refactoring. Where we once had one query and no commands we may now need nested queries and a single command along with, say, an extra integration point with a cloud service.

How much time do you need to spend rewriting those unit tests? Do you reconsider whether or not to refactor because of the burden of rewriting those tests? I know I have.

Now imaging you had no unit tests. Only acceptance, or black box, tests. Same web page. Same prime indicator. Same test. How much time do you spend writing tests when you refactor your underlying solution?

If you’ve got it right?


100% of your time writing production code. That’s what it’s supposed to be about, right? A bright idea hits you half way through your refactor. Your tests are still green. What’s the cost of experimenting?


You can commit what you have now – it’s working – and start tinkering. Your creativity starts to flow. Your repertoire of of enterprise patterns can come to the fore. Your factory factory loses its purpose: you don’t need interfaces just so you can mock, just so you can abstract.

This may seem somewhat preach, but I feel like this is the right way to do things.

More blog posts are to follow.

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