Should we write stupid code that is easy to understand for newcomers? It seems as a good thing to do. But it is the wrong thing to optimise for because it is a rare case. Most of the time you will be working with people experienced in the code base. And if there is a new member, you should not just throw her into the water and expect her to learn and understand everything on her own. It is better to optimise for the common case, i.e. people that are up to speed. It is thus OK to expect and require that the developers have certain domain and technical knowledge. And spend resources to ensure that is the case with new members. Simply put, you should not dumb down your code to match the common knowledge but elevate new team mates to the baseline that you defined for your product (based on your domain, the expected level of experience and dedication etc.).
Archive for the ‘SW development’ Category
Posted by Jakub Holý on March 6, 2016
Posted by Jakub Holý on March 4, 2016
Defensive programming suggests that we should add various checks to our code to ensure the presence and proper shape and type of data. But there is one important rule – only add a check if you know that thing can really happen. Don’t add random checks just to be sure – because you are misleading the next developer.
Posted by Jakub Holý on December 6, 2015
I have learned that it is costly to not prioritise expressing one’s design concerns and ideas early. As a result, we have a shopping cart that is noticeably slow, goes down whenever the backend experiences problems, and is a potential performance bottleneck. Let’s have a look at the problem, the actual and my ideal designs, and their pros and cons.
We have added shopping cart functionality to our web shop, using a backend service to provide most of the functionality and to hold the state. The design focus was on simplicity – the front-end is stateless, any change to the cart is sent to the backend and the current content of the cart is always fetched anew from it to avoid the complexity of maintaining and syncing state at two places. Even though the backend wasn’t design for the actual front-end needs, we work around it. The front-end doesn’t need to do much work and it is thus a success in this regard.
Posted by Jakub Holý on December 6, 2015
Cross-posted from the TeliaSonera tech blog
Alex has introduced us to the idea of front-end first design: You start by creating the front-end (browser) code. As you discover data or API calls that you need, you mock them. When the UI stabilizes, you use the mocked APIs and data to create the backend with exactly the functionality and exactly the data needed by the UI. The end result is a simpler application.
We are trying to adopt this as our approach because it is so sensible. Whenever we work with an API that wasn’t designed with the actual client needs in mind, we experience unnecessary friction and have to do various workarounds and adaptations so front-end-first absolutely makes sense to us. (E.g. when working with a REST API designed in line with REST principles – but not with our needs, resulting in a too chatty communication and more complex code.)
Of course there are same limitations. It is more challenging when you need to support different clients. And you need to take into account not just what the UI wants but also what is reasonably possible in the constraints of the existing system. You want to avoid a big gap between the two – we still remember the pain of integrating OOP and relational databases and the complexity of pitfalls of Object-Relational Mappers such as Hibernate, that try to bridge the two.
Fronted-first design rocks (for us). Try it too and see whether you too get a simpler application code and shorter time to market.
Posted by Jakub Holý on November 12, 2015
Cross-posted from the TeliaSonera tech blog
Our UX designer and interaction specialist – a wonderful guy – has shocked us today by telling us that we (the developers) are moving too fast. He needs more time to do proper user experience and interface design – talk to real users, collect feedback, design based on data, not just hypotheses and gut feeling. To do this, he needs us to slow down.
We see a common human “mistake” here: where the expression of a genuine need gets mixed in with a suggestion for satisfying it. We are happy to learn about the need and will do our best to satisfy it (after all, we want everybody to be happy, and we too love evidence-based design) but we want to challenge the proposed solution. There is never just one way to satisfy a need – and the first proposed solution is rarely the best one (not mentioning that this particular one goes against the needs of us, the developers).
Posted by Jakub Holý on October 20, 2015
Failed attempt one: Let tools do it
Originally we let
npm automatically do minor upgrades but that turned out to be problematic as even minor version changes can introduce bugs and having potentially different (minor) versions on our different machines and in production makes troubleshooting difficult.
Posted by Jakub Holý on September 1, 2015
You don’t need to finish a feature and your users don’t need to see it to be able to release and start battle-testing it. Slice it as much as possible and release the chunks ASAP to shorten the feedback loop and decrease risk.
My colleagues have been working on a crucial change in our webshop – replacing our legacy shopping cart and checkout process with a new one and implementing some new, highly desired functionality that this change enables. We have decided to decrease the risk of the change by doing it first only for product accessories. However the business wanted the new feature included and that required changes to the UI. But the UI has to be consistent across all sections so we would need to implement it also for the main products before going live – which would necessitate implementing also the more complex process used by the main products (and not yet supported by the new backend). And suddenly we had a a load of work that would take weeks to complete and would be released in a big bang deployment.
Such a large-scale and time-consuming change without any feedback from reality whatsoever and then releasing it all at once, having impact on all our sales – I find that really scary (and have fought it before). It is essentially weeks of building risk and then releasing it in a big kaboom. How could we break it down, to release it in small slices, without making the business people unhappy?
There will be failures – On systems that live through difficulties instead of turning them into a catastrophy
Posted by Jakub Holý on March 17, 2015
Our systems always depend on other systems and services and thus may and will be subject to failures – network glitches, dropped connections, load spikes, deadlocks, slow or crashed subsystems. We will explore how to create robust systems that can sustain blows from its users, interconnecting networks, and supposedly allied systems yet carry on as well as possible, recovering quickly – instead of aggreviating these difficulties and turning them into an extended outage and potentially substiantial financial loss. In systems not designed for robustness, even a minor and transient failure tends to cause a chain reaction of failures, spreading destruction far and wide. Here you will learn how to avoid that with a few crucial yet simple stability patterns and the main antipatterns to be aware of. Based primarily on the book Release It! and Hystrix. (Presented at Iterate winter conference 2015; re-posted from blog.iterate.no.)
The Are No Silver Bullets: Which Error Handling Style to Pick For a Given Configuration of Constraints?
Posted by Jakub Holý on February 18, 2015
Kent Beck in his Patterns Enhance Craft Step 3: A Few Good Solutions highlights an important fact about software development:
We encounter repeating configurations of forces/constraints that have only a handful of “solution families” and the optimal solution(s) depend on the relative weights of these constraints.
For example when deciding what error handling style we should choose when calling an unreliable rutine:
Depending on whether readability, reliability, automated analysis, performance, or future maintenance are most important you could reasonably choose any one of:
- Return value plus errno
- Exceptional value (e.g. Haskell’s Maybe)
- Success and failure callbacks
So there is no single perfect error handling style to rule them all.
Kent further explains that the forces shaping most design decisions are generated internal to the process of design, not by external constraints: whether we’re building a barn or an airport, the list of forces influencing the roofing decision is the same – snow, wind, etc. – but their relative strengths may be different. Internal forces in SW development include use of the same bits of logic repeatedly, code made for/by people, etc.. F.ex. the forces influencing naming a variable do not depend on what SW we are building but on its purpose, lifetime, etc. We encounter some configurations of these constraints again and again and a catalogue of design patterns representing the “solution families” mentioned above can guide us towards the most suitable solution for given weights.
When designing a solution, it is helpful to think in terms of these forces and their relative strengths. There is no single superior solution (a.k.a. silver bullet) as different configurations of forces and their weights might be best suited by radically different solutions. Keeping this on our minds might prevent design discussions from dengenerating into an argument.
Posted by Jakub Holý on January 10, 2015
Are you tired of days spent in front of the screen, with no results to show? Have you once again engaged in yak shaving? Today, after having failed previously, I have finally managed to solve a problem while avoiding this trap by following rigorously two guidelines preached by grandmaster programmers. Be warned: Following this approach, you will get a working solution – but you won’t like it. It will be ugly, stained by compromises, far from the elegant solution you wish for. But if your resources are limited and you want to avoid death by too many yaks, this is your only option. But first, what are these guidelines?
One: Maintain a laser-sharp focus. A great programmer is constantly aware of what she is trying to achieve and never strays far from it. If the path leads away, she backs up. If something else pops up, she writes it down for later and gets back to the job. This is essentially about deciding what not to do. (Many thanks to Kent Beck for sharing his focus secret!)