The Necessity of Consistency

A common problem with large projects starts with adding new programmers. What happens is that the new coder bypasses the existing work and jumps right into adding a clump of code. Frequently, both to make their mark but also because of past experiences, they choose to construct the code in a brand new, unrelated way. Often, they'll justify this by saying that the existing code or conventions are bad. That's were all of the trouble starts.

In terms of good or bad, most coding styles are neither. They're really a collect of arbitrary conventions and trade-offs. There are, of course, bad things one can do with code, but that's rarely the issue. In fact, if one had to choose between following a bad coding practice, or going in a new direction, following the existing practice is the superior choice. The reason for this is that it is far easier to find and fix 20 instances of bad code, then it is to find 20 different ways of doing the same thing. The latter is highly subject to missing code and bug-causing side effects, while the former is simple refactoring. 

When a programmer breaks from the team, the work they contribute may or may not be better, but it definitely kicks up the complexity by a notch or two. It requires more testing and it hurts the ability to make enhancements, essentially locking in any existing problems. And as I said earlier, it is often no better or worse than what's already there. 

It's ironic to hear programmers justify their deviations as if they were improving things, when experience shows that they are just degenerating the existing work. Once enough of this happens to a big code base it becomes so disorganized that it actually becomes cheaper to completely start over again, tossing out all of that previous effort. Overall the destruction out-weights any contributions.

That's why it is critical to get all of the programmers on the same 'page'. It is a sometimes-painful necessity to allow any large development project to achieve its full potential and it is one of the core ingredients necessary to forming a 'highly effective team'. The latter being one of the keys to building great systems; in that dysfunctional teams always craft dysfunctional software. 

With all of that in mind, there are a couple of 'rules' that are essential for any software project if it wants to be successful. The first is that all of the original programmers for a new project need to come together and spend some time to lay out a thorough and comprehensive set of coding conventions. These should cover absolutely 'everything', and everyone should buy into them before any coding begins. This isn't as difficult as it sounds, since most brand new projects start with very small tiger teams. 

The next rule is that any new programmer has to follow the existing conventions. If they don't they should be removed from the team before they cause any significant problems. It should be non-negotiable.

Now of course, this is predicated on the idea that the original conventions are actually good, but often they are not. To deal with this, a third rule is needed. It is simple idea that anyone at anytime can suggest a change to the existing conventions. For this to come into effect two things are necessary. First, the remaining programmers all need to buy into the change, and second the programmer requesting the change must go back to 'all' existing instances in the code and update them. The first constraint insures that the new change really is better than what is there, while the second one sets a cost for making the change and also insures that everything in the code base remains consistent afterwards (it also teaches people to read code and to refactor).

If teams follow the above rigorously, what happens is that the code base converges onto something organized and well-thought out. It does add some overhead, but a trivial amount relative to a messy disorganized ball of mud. And of course it means that the development project never ends for avoidable internal reasons. 

The Depth of Knowledge

A few decades back, I decided that I wanted to know more about quantum physics. I've always been driven to indulged my curiosity of various disciplines, it's been a rather long-standing obsession since I was a kid. 

I went out to the bookstores (remember those :-) and bought a huge stack of books on what might be best described as the 'philosophy' underpinning quantum physics; stuff like the wave/particle duality and Schrödinger's rather unfortunate cat. It was fun and I learned some interesting stuff. 

I'm fairly comfortable with a wide range of mathematics, so I bought a book called Quantum Theory by David Bohm. The first chapter 'The Origin of the Quantum Theory' nearly killed me. I was so hopelessly lost by the first sixteen pages. I'm sure if I dedicated years to studying, I could at least glean 'some' knowledge from that tome, but it was likely that it just wasn't possible to get there as a hobby.

Knowledge has both breadth and depth. That is, there is a lot of stuff to know, but getting right down into the nitty gritty details can be quite time-consuming as well. Remembering a long series of definitions and facts is useful, but knowing them doesn't necessarily help one to apply what they've memorized. Knowledge is both memory and understanding. My leap into quantum physics is a great example, I learned enough to chat about it lightly at a party but not enough to actually apply it to anything useful, not even chatting to an actual quantum physicist. I got a very shallow taste of the higher level perspective, but even after all those books I still didn't really understand it.

That knowledge is both wide and deep causes lots of problems. People can easily convince themselves that they understand something when they've only scratched the surface. It's like trying to make significant contributions to a field were you have only taken the introductory 101 course. If it's a deep field, there are years, decades and possibly centuries of thinking and exploration built up. What's unknown, is buried so deeply below that accumulated knowledge. Finding something new or novel isn't going to be quick. If you want to contribute new stuff, you really have to build on what is already known, otherwise you'll just be right back at the starting point looking over well-tread ground.

And some knowledge is inherently counter-intuitive. That is, it contradicts things you already thought you knew, so that you literally have to fight to understand it. Not everything we understand is correct, some of it is just convenient over-simplifications to make it possible for us to operate on a day-to-day basis. That 'base code' helps, but it also hinders our deeper understanding. We see what we need to, not what is really there.

It's pretty amazing how deep things can really go, and how often what's under the surface is so different from what lies above. My curiosity has led me to all sorts of unexpected places, one of my favorite being how much can be learned from really simple systems like boolean equations. Going down, it can feel like a bottomless pit that endlessly twists and turns each time you think you it can't go any further. With so much to grok from just simple things, it's truly staggering to contemplate the full scope of what we know collectively, and what we've forgotten. That 'field' of all things knowable has exceeded our own individual capacities a long time ago, and has steadily maintained its combinatorial growth since then. At best we could learn but a fraction of what's out there.

Software is an interesting domain. It's breadth is huge and continuously growing as more people craft their own unique sub-domains. A quick glance might lead one to believe that the whole domain is quite shallow, but there are frequently deep pools of understanding that are routinely ignored by most practitioners. Computational complexity, parsing, synchronization, intelligence and data mining are all areas that sit over deep crevices. They are still bottomless pits, but depth in these areas allows programmers to build up the sophistication of their creations. Most software is exceptionally crude, just endless variations on the same small set of tricks, but some day we'll get beyond that and start building code that really does have the embedded intelligence to make people's lives easier. It's just that there is so much depth in those sort of creations and our industry is only focused on expanding the breadth at a hair-raising pace. 

It's easy to believe that as individuals we posses higher intellectual abilities, but when you place what our smartest people know along side our collective knowledge, it becomes obvious that our inherent capabilities are extremely limited. Given that, the rather obvious path forward is for us to learn how to harness knowledge together as groups, not individuals. Computers act as the basis for these new abilities, but we're still mired in the limits of the personalities involved in their advancements. Software systems, for example, are only as strong as their individual  programmers and they are known to degenerate as more people contribute. We only seem to be able to utilize the 'overlap' between multiple intellects, not the combination. That barrier caps our abilities to utilize our knowledge.

Despite all we know, and all of the time that has passed, we are still in the early stages of building up our knowledge of the world around us. If you dig carefully, what you find is a landscape that resembles swiss cheese. There are gapping holes in both the breadth and depth of what we know. Sure, people try to ignore the gaps, but they percolate out into the complexity of modern life at a mind numbing rate. We rooted our cultures on a sense of superiority over the natural world, but clearly one of the largest things we still have to learn is that we are not nearly as smart as we think we are. If we were, the world would progress a lot smoother than it has been. Knowledge is after all, a very significant way to avoid having to rely on being lucky.

Estimation

One of the most persistent myths in software development is that it is impossible to estimate the amount of programming work required for a project. No doubt this is driven by people who have never been put in a position where estimates mattered. It is indeed a very difficult skill to master but a worthwhile one to have, since it routinely helps to make better decisions about what to write and when to write it.

In my younger days I was firmly in the camp that estimates were impossible. That changed when I got a job working with a very experienced, veteran team of coders. I whined that it was "hopeless", but they said to "just try it", so eventually I did. We settled on lines-of-code as the metric, and over the years we tracked how much we wrote and how large the system was. Surprisingly, after years of tracking our progress, we found that we could make quite strong estimates about our future work output.

After I left that job, I found myself in a startup that was particularly dependent on selling custom code to the clients. It accounted for a significant part of our revenue, and it all relied on being able to produce quick, accurate estimates during client meetings. Estimate too high and they wouldn't buy in. Estimate too low and we'd lose money on the work. Since it was just after the dotcom bomb, things were really tricky so we needed every penny we could get to avoid bankruptcy. My earlier experience really paid off. I would sit in meetings, noting down all of the pieces in discussion, then be able to quickly turn these features into dollars so that we could hammer out an appropriate set of functionality that matched back to the client's budget. That on-the-fly type of price negotiation could have been really dangerous except that I was quite accurate with calculating the numbers. When you have no alternatives, you get forced to sharpen your skills.

For the rest of this post I'll do my best to describe how I estimate work. There isn't a formal process and of course there are some tricky tradeoffs to be made, but with plenty of practice anyone can keep track of their past experiences and use that to project future events. It's a skill, a very useful one and one that really helps with ensuring that any software project does not get derailed by foreseeable issues.

The first thing that is necessary is to choose a reasonable metric for work. Although it is imperfect, I really like lines-of-code (LOC) since it's trivial to compute and when used carefully it does reasonably match the output of a development project. It's also easy for programmers to visualize.

Most of the systems I've worked on have been at least tens of thousands of lines, often hundreds of thousands, so to simplify this discussion I'll use the notation 10k to mean 10,000 LOC, it will just make it easier to read.

For me, I count most of the lines including spaces and comments, since they do require effort. I generally avoid counting configuration files, and for web apps I usually separate out the GUI. Coding in languages like HTML and CSS is often slower than the more generalized languages like Java or C#. This causes a multiplier effect between different programming languages. That is, one line in Java is worth two lines in straight C, since the underlying primitives are larger. Tracking the differences in multipliers due to language or even the type of code being written is important to being able to assess the size of the work properly.

I generally like to watch two numbers, the first is the current running total of lines for the system. So we might talk about a 150k system or perhaps a rather smaller 40k one. If along the way someone replaces 5k with a better 10k piece, the total has only increased 5k. That is, modified lines count for nothing, deleted lines are subtracted and new lines are added. It's only the final total in the repo that really matters. It's easy to calculate this using a command line tool like word count, i.e. "wc -l *.java". One of the first scripts I write for any new job is to traverse all of the source and get various counts for the existing code. The counts are always tied to the repo. If it isn't checked in, it hasn't been completed.

The second number I like to watch is any single developer's production. That is the total number of lines added to the repo per year (although sometimes I talk about weekly numbers). So I think of programmers as being able to contribute say 20k or 50k. Sometimes I think in terms of new lines, sometimes in terms of net lines, depending on the level of reuse in the code. Again, language and code type are also significant, you can't compare a 15k JavaScript programmer to a 25k C# coder, but knowing the capacity of the coders on a given team really helps in determining if any particular project is actually viable.

One big issue to watch out for are the copy-and-pasters. They might have awesome numbers like 75k, but the messiness of their output and the dangers of their redundant code really reduces their work to 1/3 or worse of their actual counts. That, and they endanger any ability to safely extend their code. If you were to try and capture the counts, the copied code would count for 0 lines, modifications and additions would add, but deletes would be ignored.

Now generally a 50k programmer might have lots of 1k weeks, but very few programmers are actually consistent in their output and modern software development is highly disruptive. It's a lot of 'hurry up and wait". Back in the waterfall days, when the development stage lasted months or years it was easier to be consistent for long periods, and it also meant higher yearly counts. But even then some weeks will have very low counts, while others might be over 2k. Because of that variability it is more appropriate to look at the yearly numbers. As the project matures, the counts should get lower as well. That happens, particularly if the software is well-written, because the size of the underlying primitives grow. Instead of having to start from scratch each time, most new work can leverage the existing code (if the counts aren't going down, then possibly the cut & pasting is increasing).

Code that is algorithmically complex or is particularly abstract is obviously a lot slower to write and debug. In that sense, the backend guy from a project will progress much slower than the front end guy. So you might have a domain expert coder at 15k, with a GUI front guy at 35k. A decent generalized piece might have really low counts, but when it's reused each time it saves the overall system size from bloating, although it doesn’t reflect directly in the numbers.

Thinking in terms of totals and capacity has the nice secondary attribute that it makes it easy to translate into man-years. For example, a small but functional enterprise web-app product needs at least 100k to round out it's functionality (including administration screens). For a 50k coder that's at least 2 man-years. If you see a 350k system, you can get a sense of how long it might take to rewrite it if you had a team of 25k coders, it's sitting at about 14 man-years to replace unless you can get away with a lot less code.

My suggestion to most programmers is to learn to track both numbers for themselves. They don't have to share them with management, but understanding their own abilities to produce code and the size of the projects that they've experienced really helps in knowing their current skill level. That keeps programmers from over-promising on their assignments. If you are a 30k programmer and someone wants you to hammer out a 40k web app in half a year, you can be pretty sure that you're in trouble coming right out of the gate. If you know that in advance -- as opposed to the last month or so -- you can start taking action to address the issue, such as asking for more resources or reducing expectations (or reducing the quality).

It's also worth noting that speed isn't what counts, it's organization and quality that really matter. I would be far happier with a detail-oriented 15k programmer for which we never have to debug any of their work or rewrite it, than a reckless 75k programmer that is essentially just contributing more problems. Projects last forever, so it's important to get the work done well, encapsulate it and then move on to other more interesting code. A well-rounded team usually has a mixture of fast and slow programmers, with hopefully a wide range of different skills.

My first code tracking experiences came when building a massive 150k distributed cache. In the early years I was cranking out about 50k per year as my primary project (I've always had at least one secondary project that was not intended for production, but just to sharpen my skillset). Years later, I was working on a 120k enterprise product in Perl at about 30k per year (with lots of sales and management duties). These days I'm somewhat less than 15k in C# on a 350k behemoth (which if it wasn't for cut & paste should have been closer to 150k), but again my role isn't just pure coding anymore.

Size matters. I've worked on a few >1M systems, but at that level there are usually teams that are responsible for 50k - 200k chunks of the system. From experience I've found that a code base over 50k - 100k starts to get unwieldy for a single individual to handle by themselves. Most single developer in-house systems tend to range from 25k - 50k. Commercial systems have to be larger (to justify the sales) and tend to be higher quality so there are more lines; they usually start around 100k and go into the millions (except possibly for apps). Quality requires more code, but reuse requires quite a bit less, and there can be big multipliers such as 2x or even 10x for brute force and cut & paste. It's those latter multipliers that always makes me push reuse so hard. Time might be saved from pasting in code, but it gets lost again in testing, bugs and obfuscating any attempt to extend the work. In a big project, bad multipliers can result in the work falling "years" behind schedule particularly if the work is compared to a competitor with an elegant code base. If you have to do three times the work, you'll have 1/3 of the features.

Once you've mastered the skill of tracking, the next thing to learn is estimating. I always use ranges, so a new bit of work might be 10k - 20k in size. The best and worst cases are drawn directly from experience with having written similar code. The ranges get wider if there is a lot of uncertainty. They are always relative to the system in question, so that a new screen for one rather brutish system might require 20k in code, but for an elegant one with lots of reuse that might be 5k or less (getting below 1k is awesome).

Of course estimating in an ongoing project is easier than a green field one, but in some sense if a programmer isn't experienced enough to make a reasonable estimate he or she might take that as a sign that they don't currently have enough experience to get the work completed properly. Most system extensions are essentially horizontal, that is they are just adding in features that have the same design patterns as existing features. In that case, if the new work leverages existing work, then a huge amount of time can be saved per feature and the code will automatically maintain good consistency (and have less testing).

I pretty much expect that something similar to the Pareto rule works for overall complexity. That is between 10 and 20 percent of the system is difficult, slow code while the rest is fairly routine. The 80% still requires work, but it should be quite estimable with practice. The difficult stuff is nearly impossible to estimate since unexpected problems creep up, which is why each key aspect of it should be prototyped first during the analysis and/or design phase (for startups this is essentially version 1.0). If you separate out the code into independent problems then it makes it easier to plan accordingly. Often under-estimates in some parts balance against over-estimates in others.

Another common issue is uncontrolled scope creep, where the underlying requirements are constantly shifting. Sometimes that is just a lack of reasonable analysis. Since analysis is way cheaper than code the development should be put on hold until the details are explored more thoroughly. It's always faster than just flailing at the code. Sometimes scope creep is caused by natural shifts in the underlying domain, in which case the design needs to be more dynamic to properly solve the real problem.

It's worth keeping in mind that depending on the testing requirements and the release procedures, a coding estimate is only a fraction of the work. Testing, in reasonable systems tends to stay in lock step as a fraction of the coding work. Releases are more often a fixed amount of effort.

Once you can size incoming work, the next trick is to be able to shift the team around to get it done effectively. Programmers are like chess pieces, they all differ in strengths. It doesn't work to give a backend guy some GUI piece if you have a faster front end guy available for the job. This is spoiled of course since some programmers don't want to focus on their strengths, they want assignments that stretch their abilities. With good estimations, it actually makes it easier to let them try out different parts, since tracking lets you know how much work is remaining and whether there is actually some slack time available. High efficiency teams are generally ego-less such that the programmers aren't siloed, they can work on any section of the code at any time. Of course having at least two coders familiar with every part of the system reduces disruptions with emergencies, holidays and people leaving. It also helps with communication and overall quality. But it has to be managed in a way that doesn't just blindly assume that all programmers are interchangeable cogs. Each one is unique, with their own skills and suitability, thus it takes plenty of observations and some deep management skill to deploy them well.

For me, with deadlines, I'll either accept a variable number of features with a hard date, or the full set of features with a variable date. That is, we can deliver 4 - 10 features on date X, or we can deliver all 10 features at some floating time in the future. Sometimes the stakeholders argue, but generally there are one or two really key things they are after anyways. With reasonable estimations, fixed dates are manageable. You can choose to do a couple of priority items early, then go after some low hanging fruit (easy items), then just aggressively cut what isn't going to make it. A key trick to this approach is to get the developers working on just one or possibly two items at a time, and don't let them go on until the items are done and dusted. Estimations really help here because you can quickly see if any of the items exceeds the length of the next iteration, and thus should be branched off from the main development (and then not tied up in cross dependencies). Obviously the more accurate the estimates, the more options you have in planning the development and managing the expectations of the stakeholders. Accurate estimates reduce a lot of the anxieties.

Once a project gets established, the incoming workload tends to fluctuate. That is, it's quiet for a while, then a whole load of new work comes in all at once followed by a rush to get it done. Software development is best when it is a smooth and steady process, so being able to scale the work loads and re-arrange the teams really helps in smoothing out the development to a nice consistent pace. This avoids destructive practices like 'crunch mode' and allows for intermediate periods to reduce technical debt. It also makes it easier to decide when to just hack up some throw-away code and when to take the time to generalize the code so that you can leverage it later. Foresight is an incredibly useful skill for big development projects.

What gets a lot of projects into trouble is that become reactive. They focus on just trying to keep up with the incoming work, which ultimately results in very high technical debt and further complicates the future workload. Learning to estimate is tricky, but valuable as it helps to make better decisions about what to write and when to write it. This helps in getting ahead of the ball in the development, which leads to getting enough 'space' to be able to build clean, efficient and high quality systems.

Once a programmer has mastered the basics of coding, they start getting the itch of wanting to build larger and larger systems. For medium sized systems and above the technical issues quickly give way to the architectural, process and management issues. Within that level of software development, life is much easier if you have the skills to size both the work and the final system. It allows significant planning and it provides early indicators of potential trouble.

Estimating is a tricky skill and one that can not be trivially formalized, but it's not impossible and once you have enough mastery you really wonder how you ever lived without it. Nothing is perfect, estimates less so, but with practice and experience software developers can produce very usable numbers for most of their development efforts, and the technical leads can understand and utilize the underlying capacity of their team members. Estimations are not feasible from non-programmers and they should not be used to rank programmers against each other. There are very real limits to how and why they can be practical, but when used correctly they can at least eliminate many of the foreseeable problems that plague modern development efforts. A little less chaos is a good thing.