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04 July, 2015

The following provides no answers, just doubts.

Technical debt, software rot, programming practices, sharing and reuse etcetera. Many words have been written on software engineering, just today I was reading a blog post which triggered this one.

Software does tend to become impervious to change and harder to understand as it ages and increases in complexity, that much is universally agreed on, and in general it's understood that malleability is one key measure of code and practices that improve or retain it are often sensible.

But when does code stop to be an asset and starts being a liability? For example when should we invest on a rewrite? 
Most people seem to be divided in camps on these topics, at least in my experience I’ve often seen arguments and entire teams even run on one conviction or another, either aggressively throwing away code to maintain quality or never allowing rewrites to capitalize on investments made in debugging, optimization and so on.

Smarter people might tell you that different decisions are adeguate for different situations. Not all code needs to be malleable, as we stratify certain layers become more complex but also require less change, new layers are the ones that we actively iterate upon and need more speed. 
Certainly this position has lots of merit, and it can be extended to the full production stack I’d say, including the tools we use, the operating systems we use and such things.

Such position just makes our evaluation more nuanced and reasonable, but it doesn’t really answer many questions. What is the acceptable level of stiffness in a given codebase? Is there a measure, who do we ask? It might be tempting just to look at the rate of change, where do we usually put more effort, but most of these things are exposed to a number of biases.

For example I usually tend to use certain systems and avoid others based on what makes my life easier when solving a problem. That doesn’t mean that I use the best systems for a given problem, that I wouldn’t like to try different solutions and that these wouldn’t be better for the end product. 
Simply though, as I know they would take more effort I might think they are not worth pursuing. An observer, looking at this workflow would infer that the systems I don’t use don’t need much flexibility, but on the contrary I might not be using them exactly because they are too inflexible.

In time, with experience, I’ve started to believe that all these questions are hard for a reason, they fundamentally involve people. 
As an engineer, or rather a scientist, one grows with the ideal of simple formula to explain complex phenomena, but people behaviour still seems to elude such simplifications.

Like cheap management books (are there any other?) you might get certain simple list of rules that do make a lot of sense, but are really just arbitrary rules that happened to work for someone (in the best case, very specific tools, worst just crap that seems reasonable enough but has no basis), they gain momentum until people realize they don’t really work that well and someone else comes up with a different, but equally arbitrary set of new rules and best practices.
Never they are backed by real, scientific data.

In reality your people matters more than any rule, the practices of a given successful team don’t transfer to other teams, often I’ve seen different teams making even similar products successfully, using radically different methodologies, and viceversa teams using the same methodologies in the same company managing to achieve radically different results.

Catering to a given team culture is fundamental, what works for a relatively small team of seniors won’t apply to a team for example with much higher turnover of junior engineers. 
Failure often comes from people who grew in given environments with given methodologies adapted to the culture of a certain team, and as that was successful once try to apply the same to other contexts where they are not appropriate.

In many ways it’s interesting, working with people encourages real immersion into an environment and reasoning, observing and experimenting what specific problems and specific solutions one can find, rather than trying to apply a rulebook. 
In some others I still believe it’s impossibile to shut that nagging feeling that we should be more scientific, that if medicine manages to work with best practices based on statistics so can any other field. I've never seen so far big attempts at making software development a science, deployed in a production environment. 

Maybe I'm wrong and there is an universal best way of working, for everyone. Maybe certain things that are considered universal today, really aren't. It wouldn't be surprising as these kinds of paradigm seem to happen in the history of other scientific fields.

Interestingly we often fill questionaries to gather subjective opinions about many things, from meeting to overall job satisfaction, but never (in my experience) on code we write or the way we make it, time spent where, bugs found where and so on...
I find amusing to observe how code and computer science is used to create marvels of technological progress, incredible products and tools that improve people’s lives, and that are scientifically designed to do so, yet often the way these are made is quite arbitrary, messy and unproductive.
And that also means that more often than not we use and appreciate certain tools we use to make our products but we can’t dare to think how they really work internally, or how they were made, because if we knew or focused on that, we would be quite horrified.

Software science does exist, in many forms, and is almost as old as software development itself, we do have publications, studies, metrics and even certain tools. But still, in production, software development seems more art than science.