Metaphors in Software Engineering

One metaphor frequently used in the field of software development is the metaphor of software architecture. The architecture of a software system consists, like the architecture of building,  of the main structures of the systems.

For a software system, the term “structure” could mean structures that are logical or conceptual. They don’t necessary match with tangible system boundaries. But the term “structure” does mean that the metaphor is biased towards expressing the static aspects of the software system.

Unlike buildings, software systems have also dynamic aspects. Information flows in a software system, and systems communicate with each others. Therefore, other metaphors can be useful to explain the nature of software systems.

Here are a few that I find interesting.

A city

As said, the architecture metaphor is limited in that if focuses too much on the statics. The city metaphor is in this regard better, since it evoques simulateously static structures (roads, bridges, buildings) but also dynamic aspects (traffic flow, people living in the city). Good city planning deals with both. The metaphor can be used for a software system, but also for collections of software systems.

Enterprise architecture is the field of IT that addresses IT strategy at the enterprise level. The city metaphor is a good own for the enterprise architectture. Changes of IT strategy (for instance, moving to the cloud) impact many systems and take years to be achieved. They significantly and durably change the way software system are built for the enterprise. If Hausmann’s renovations gave a new face to Paris, moving to the cloud will give a new face to the IT of your enterprise. 

A garden

The architecture metaphor is also limited in that it conveys the impression that a software is built once, and then never changes. It may be true for a building, but isn’t for software systems. According to the laws of software evolution, a software system must constantly be maintained and adapted to the needs of their users, or it will become useless. As software systems are developped and grow, they tend to accumulate inconsistencies that must be actively removed. This is much like a garden, which must be constantly maintained, and bad weeds, which must be removed. 

It’s possible to convey something similar with the architecture metaphor too, since building suffer wear and tear. We speak sometimes of architecture erosion, to denote the degrading quality of the architecture. By the way, buildings do change over time, sometimes quite significantly.

A book

Software is expressed using programming languages and its source code consists of text. A software system can thus be compared to a book, albeit a very special one. You can’t read it linearly and everything is interlinked. But there is a sense of style in a given code base, and code can be more or less elegant. There is something arful to programming. Given that developpers spend a lot more time reading code than writing code, taking care of software as text makes sense. With development approaches like literate programming, developpers a supposed to write the source code like a story to explain their thoughts. It didn’t catch on, but still worth a look.

A living organism

A running software system can also be compared to a living organism: it needs energy to run and do something useful. In some way, functions of the runtime, like memory management or thread scheduling, can been seen as some form of metabolism. Interestingly, some software systems like blockchains are explicitly designed to have an inefficient metabolism and consume large amount of energy. A running software system has a health too, which indicates how well the system works. Millions of things can go wrong during run time, degrading its health and behavior. For instance a memory leak will over time degrade the performance of the system until it simply dies.  Some components of a software system have at run time multiple instances. A failure of one component doesn’t break the whole system, just like we can live with one kidney. A running software systems can be compromised by a hostile inputs, the equivalent of a pathogen. The immune system of a running software consists of mechanisms like SQL sanitization, managed memory, safe pointers, etc. which aim at making software more robust. Usually software systems do not reproduce, though. Except for software viruses.

An asset

The IT has long been seen as a cost center, detached from business units that are profit centers. With digitalization, the perception is changing. Software is the enabler for the business, and go hand in hand with it. It is an asset and generates value. But with software, more code doesn’t mean more returns. More code means more maintenance, and only some feature of the system might actually deliver value.

There are of course more metaphors. Just have a look at the links below. The city, the garden, and the book metaphor are somewhat popular. The metaphor with living organisms is surprisingly uncommon. The asset metaphor isn’t really a metaphor- more like a mindset. The architecture metaphor is sometimes critiqued, but if we assume that software development is an eingeering discipline, it’s the only metaphor that resonates with engineers. So it’s unlikely to change.

More

Chasing the Perfect Technology

The goal of pretty much any framework/plattform that you use — from a PaaS offering to application server and everything in between — is to make you more productive by taking care of some technical complexity for you: “Focus on the business logic, not the technology”.

Frameworks and platforms speed up development so that you can ship faster. And it’s true that you can ship faster: You can now, with current technologies, build an internet-scale service, highly available, able to handles millions of transactions per seconds, in a few month. It would have been unimaginable one decade ago.

The peak of productivity is achieved when you master your stack completely. You can then spend significant time working on business feature with little friction around technology itself.

Sadly, the peak of productivity is rarely reached.

One of the reasons is that developers get bored too early. Once the technical groundwork is in place and you just have to use it, it becomes boring. It’s fun to set up a whole analytics pipeline to solve this first analytics problem. Using the exact same pipeline to solve another problem? Boring.

Go ask a developer to use your existing infrastrucutre and stack as is and simply implement new features. I bet they will be lukewarm if they don’t see any technological problem to solve, or at least some technology to learn.

I speak from experience. The project I’m working on is an application for which a dedicated platform was built. This platform provides all sorts of thing to write applications, ranging from messaging, message processing, fault tolerance, configuration management, job scheduling. You can reuse these buildings blocks to design new features. As long as features requires new combination of building blocks, it’s interesting. But once it feels like using the same pattern every time, it becomes boring, even if it’s actually the moment you’re the most productive.

What motivates developpers is leveraging technologies to solve a problem. They are interested in figuring out how to use technology for your problem, not actually having more time writing business logic. Engineers have studied computer science because they like technology more than other business domains.

Technology platforms and frameworks – app servers, cloud, data pipelines, web framework, etc. – are so amazingly complex that you will need to solve several problems with them before you feel like you master them. Also, even if you master the technologies individually, the combination of the technologies might pose some new challenges. At the same time, technology changes very fast. This is another reason why we rarely reach the peak  productivity: technologies change before we truly master them. Technology evolves fast and we’re always playing catch-up.

A VM is for instance way easier to deal with than physical hardware. Using VMs definitely improves productivity. But as soon as you have VMs you want to become elastic. And for this, you need a whole you set of tools to learn and master. Progress in technology takes the form of layers that piles on. When you’ve barely master the first layer comes already the second one. These new layers, once mastered, enable new jumps in productivity though.

Not reaching peak productivity isn’t in itself a problem, since productivity grows nevertheless. Curiosity is what makes us push technology. What’s interesting is to realize that productivity and curiosity are actually at odd. It’s because we are curious that we never truly master our technologies and don’t reach peak productivity. But it’s also because we are curious that productivity in the long term always increases.

More

In fact, we anticipate that there will soon be a whole generation of developers who have never touched a server and only write business logic.