"Computer Science" is Not Science and "Software Engineering" is Not Engineering

Updated 3/12/2005

I have been in a lot of "software engineering" debates, perhaps too many. It is frustrating that there are heavy opinions about the "right way" to make software, but no easy way to objectively compare them to settle long-standing arguments. I have been pondering and studying why this is the case, and think I am finally able to articulate an answer.

If the software discipline is "science", then the scientific process should be available to settle arguments. But it seems to fail. Some suggest that instead it is "engineering", not "science". But engineering is nothing more than applied science. For example, in engineering, bridge designs are tested against reality in the longer run. Even in the short run, bridge models can be tested in environments that simulate reality. Simulations are a short-cut to reality, but still bound to reality if we want them to be useful. If a bridge eventually fails, and the failure is not a construction or materials flaw, then what is left is the engineering of the bridge to blame. An engineer's model must be tightly bound to the laws of physics and chemistry. The engineer is married to the laws whether he/she wants to be or not.

But we don't have this in software designs for the most part. We have the requirements, such as what the input and output looks like and the run-time constraints which dictate the maximum time a given operation is allowed to take. But there is much in-between these that is elusive to objective metrics.

(many solutions illustration)
There are often many techniques and algorithms
to produce the same results. However, beyond
being able to solve it, useful and objective
metrics to select the "best" solution are lacking.
("Method" not to be confused with OOP "method" here.)

Most of the techniques and paradigms under common debate can usually deliver the requirements. This is because they are "Turing equivalent", which basically means they are capable of implementing any clearly-specified algorithm, given enough time and resources. The bottom line is that delivering the required results is not a distinguishing factor; they can all do it.

There are some specialties, such as Artificial Intelligence, where the answer is not necessarily "fixed". The "answer" is judged more or less on a continuous scale. In these situations different algorithms are compared and the results are ranked. For example, a metric similar to a lab-rat maze test for intelligence testing can be performed to evaluate performance. However, just because a given algorithm is known to be better, this does not tell us if it is the "right" or only possible algorithm. It might be the "best known" at a given point in time, but not much can be concluded beyond that. As expounded upon later, we don't know what we don't know.
Some options under debate may run slower, but that is usually not the key point in debates. Somebody will argue that higher developer productivity makes up for the slower speed (or need for bigger machines) or that in a few decades chips will be fast enough such that it does not matter. Developer productivity is possibly another metric that is measurable, but is still elusive because there too many variables. I will return to productivity later because it is indeed an important issue.

So, if physical engineering is really science ("applied science" to be more exact), but software design does not follow the same pattern, then what is software design? Perhaps it is math. Math is not inherently bound to the physical world. Some do contentiously argue that it is bound because it may not necessarily be valid in hypothetical or real alternative universe(s) that have rules stranger than we can envision, but for practical purposes we can generally consider it independent of the known laws of physics, nature, biology, etc.

The most useful thing about math is that it can create nearly boundless models. These models may reflect the known (or expected) laws of nature, or laws that the mathematician makes up out of the blue. Math has the magical property of being able to create alternative universes with alternative realities. The only rule is that these models must have an internal consistency: they can't contradict their own rules. (Well, maybe they can, but they are generally much less useful if they do, like a program that always crashes.)

Software is a lot like math, and perhaps is math according to some definitions. The fact that we can use software to create alternative realities is manifested in the gaming world. Games provide entertainment by creating virtual realities to reflect actual reality to varying degrees but bend reality in hopefully interesting ways. A popular example is The Sims, which is a game that simulates social interaction in society, not just physical movements found in typical "action" games.

Most games tend to borrow aspects or laws of the physical world. This is because game purchasers are more likely to buy something that they can relate to in one way or another. But making worlds that have little or nothing to do with the physical world are also possible. Worlds can be created where anti-gravity is plentiful, for example. Things would fall up. Or, time could run backward, sideways, bump into other time- lines, etc. (I know, I watch too much Star Trek, I admit.)

One could also play with social and economic rules that we normally assume are fixed or static. Researchers have even evolved simple artificial life-forms using "genetic algorithms" in order to experiment with the Darwinian theory of natural selection. It involves artificial food, artificial energy, artificial sexual and asexual reproduction, etc. The only limitation is the imagination of the creator of the virtual world (and perhaps the pesky limitations of computer resources). As long as they can define the rules clearly, they can make a universe that follows those rules. They build the rules into software, press the "run" button, and then sit back and watch. If you have an urge to play God, software is currently the best game in town. Unlike your in-laws or baby brother, the computer won't complain about your attempt at domination.

This nearly infinite flexibility of software, I have concluded, is why objective software design evaluation is nearly impossible: there is no objective reality inside software. This is the secret cause of all the debate headaches. We might as well be a bunch of loonies in the mental hospital arguing over which one of us is the real or best Napoleon. The difference is that in cyberspace we can all be Napoleon. Some have suggested that the reasons for lack of objective metrics are because we don't know enough yet; that we just haven't learned how or what to measure. Instead, the problem is that we can manufacture what we "know". Today you are Santa Clause, tomorrow Cleopatra. This can be both in a literal sense, such as a role-playing game, or a figurative sense, such as software organization principles.

In software design there are many possible different design paths to provide the correct answer. But if there are many solutions to the same problem, which one is "better"? One possible answer is, "maybe it does not matter". As long as the software takes the input and produces the right output, then why care how it works? If a black box works, then why worry about how the innards work?

One of the reason the innards do matter is because different programmers have to maintain (fix or change) software code made by others. Using our God analogy, if God goes on vacation or retires, then his replacement needs to know how the world works in order to keep it running. Real gods may have infinite comprehension skills such that they can quickly figure it all out, but humans have built-in comprehension limitations. It costs us time and money to figure out how something works.

Thus, it is nice to have conventions that make it easier for person A to understand the work of person B. But the next logical follow-up question is whether some conventions are inherently "better" than others. Conventions alone may help with communication, but are all conventions created equal?

Since these conventions are related to communication, we must know more about the communication process in humans to answer the "better" question. But this seems inherently tied to psychology. If our answers depend on knowledge of human psychology, then we cannot yet claim we have objective tools to compare conventions because psychology by definition is about subjectivity. Plus, psychology is still an immature field of study compared to say physics. Further, every brain is different. A truth or pattern found in brain A may not necessarily also be in brain B.

Maybe when the human brain is finally fully decoded and we understand how it works in detail, then Computer "Science" will graduate into a hard science. Further, there has not been sufficient cooperation between psychology researchers and software researchers. Most psychology research is geared toward curing or reducing "big" problems, such as extreme depression, schizophrenia, etc. Barring finding a buried treasure, that is where most of the research effort understandably goes. "Industrial psychology" will only receive pocket change in comparison, but it will probably take much more than that to answer the big questions.

Ironically, if we knew enough about brains to answer such questions, we could probably use such knowledge to build electronic versions and make the need for human programming mostly obsolete. It is true that the first models will probably fill a room, but based on past patterns it wouldn't take long to shrink the technology and mass manufacture it.
Now we come back to programmer productivity, which is the second reason to be concerned with the innards of our black box. If virtual words can be built, changed, and debugged faster, then companies can save money and hopefully make the economy more efficient. Thus, focusing on productivity techniques is clearly a useful endeavor.

The problem is that there does not seem to be many studies done in this area. Some claim that the size of code corresponds to productivity such that a program with 2,000 lines or tokens is easier to write and maintain than one that is 10,000 lines or tokens. But there are heavy debates on both sides about whether size alone translates into productivity. For example, Perl code can often be written to be small in size, but many find it notoriously difficult to read. It also depends on the problem domain. Some of the best examples of small code I've ever seen are from "collection oriented" languages, such as APL's derivatives. However, they were toy problems that are hard to extrapolate into many real-world situations.

There are just too few good direct studies on productivity. For example, researchers could lock a bunch of programmers in different rooms with the same specification. Each room would use a different language or paradigm and we then see who finishes first. However, there are complications to this. Code that runs is not necessarily code that makes for easy maintenance, and many languages and paradigms are allegedly geared to optimize maintenance, not initial product delivery. We can't keep the test subjects sequestered for years on end just to test long-term maintainability. No viable company or government is going to allow this with real projects.

My personal observation in combination with bits of Edward Yourdon's studies suggest that the paradigm or language that a given developer is most comfortable with is the one that makes them the most productive. This would mean that to get optimum productivity, hire a bunch of like-minded developers who are fanatics of a given language or tool.

The bottom line is that empirical science is sorely lacking in our field. If that's the case, then what is in all those volumes of "computer science" books that are available? They gotta be writing about something in all those since they are not blank pages.

There are various techniques, idioms, tools, etc. commonly used in software, and most of the academic writings seem to be about these. Examples include:

  • Boolean Algebra
  • Object Oriented Programming
  • Data Structures (lists, sets, queues, stacks, etc.)
  • Algorithms (sorting, searching, traversing, etc.)
  • Relational Theory
  • Set Theory
  • Type Theory

But are these concepts "science"? Let's explore one of the simpler ones: Boolean algebra. Boolean algebra is a mathematical idiom that deals with Boolean operands and operators. Operators commonly used are AND, OR, and NOT. This is not science though, it is math.

But almost everybody agrees that it is a useful math. About the only widely-used competitor is 3-value logic, which uses a 'nil' or 'null' in addition to the Boolean operators. Many RDBMS use this to deal with null's. But 3-value logic is still a close cousin to Boolean algebra.

Note that the hardware may require Boolean states, but the software does not necessarily also need it. Programming languages running on Boolean hardware can emulate the alternatives, such as 3-value- logic, perfectly well. A match between software and hardware may result in speed improvements, but otherwise they are independent issues. Also note that the use of null in RDBMS is controversial among relational guru circles.

While it may be theoretically possible to develop software based on something besides Boolean algebra and its derivatives, most would agree it is difficult. This is partly because nobody knows about any alternatives that they can relate to. Seven-value logic may also be able to produce Turing-complete languages, but only extreme oddballs would want to use it. It is easy to win a contest when there are no viable contenders. However, in other areas there are viable alternatives that tend to compete with each other. Relational and OOP are one example. If there are competitors, then we need the rigor of the scientific method. Anecdotal evidence is not enough. It often conflicts and is difficult to verify.

Boolean algebra is still math, not science. If we want to turn the issue into a science issue we need to turn it into an empirical claim, such as "Boolean algebra makes code shorter than Brand X". But, shorter code is not necessarily "better" code, as described above. Somebody could perhaps be just as productive with longer code if it offers other benefits. Let's review some of the metrics kicked around so far:

  • Performance - How fast the program runs
  • Productivity - How fast a programmer can create and/or maintain a program
  • Consistency - Consistency allegedly results in better inter-programmer communication and simplifies training.
  • Code Volume - Less code allegedly makes code reading and editing easier.

This list may not be exhaustive or perfect. However, let's not dwell on finding perfect metrics here. It serves mostly as an example of the kinds of things that need to be measured in order to turn software issues into "science".

If you look at the "computer science" (CS) literature, you don't see such issues addressed often. Only the first one (performance) seems to have a fairly large body of written knowledge that implies it is "science". For example, in the 1970's much was written on searching and sorting algorithms and their performance using "Big-O" notation. But beyond performance there is a huge void.

Most of the CS literature fits the pattern found in a typical book or chapter on Boolean algebra. Generally this is the order and nature of the presentation:

  1. Givens - lay out base idioms or assumptions
  2. Play around with those idioms and assumptions to create a math or notation
  3. Show (somewhat) practical examples using the new notation or math
  4. Introduce or reference related or derived topics

A Boolean algebra book would describe the basic operators and operands, and introduce more complex operators that are based on the simpler ones. For example, it might show how "XOR" and "EQV" can be made with the root operands of AND, OR, and NOT. (Actually only one is needed for the base: "NAND". All the rest can come from this alone).

Then such a book may introduce practical examples, such as the field of formal logic and computer algorithms. Finally, it might suggest further topics, such as 3-valued-logic (mentioned above) and maybe introduce or give references to Relational algebra and set theory. These make use of Boolean algebra also.

This may be all interesting stuff, but it is not science. It is not necessarily bound to the physical or "real" world. The practical examples section is suggestions, not evidence. Rarely is it compared to alternatives outside of performance.

In a few cases where comparisons to alternatives are made, often only the down-sides of the alternative are given, and not the up-sides. For example, some books show patterns of change that favor the author's pet paradigm or language, but conveniently ignore change patterns that don't favor it. Good comparisons have to be thorough and should try to get both "sides" of the story, ideally letting experts and/or proponents of both sides comment on each comparison item. The problem is that a good comparison alone would probably fill a large book itself. And, such comparisons often end up relying on subjective psychological factors in the end anyhow. "Slam dunk" evidence is rarely uncovered. Objective silver bullets are rare.
Remember "imaginary numbers" from algebra? You could do funky stuff with them like take the square root of negative nine. Imaginary numbers are generally useless for the physical world that we experience. However, they turned out to be very useful in the field of electronics. More specifically, they had predictive power in the field of electronics. Using imaginary number math, one can predict behavior of electrons and the accuracy of such predictions in models can be measured.

One could argue that Boolean algebra (BA) also has predictive power, but it is weaker kind of prediction. Computer circuits use Boolean as their underlying language, so BA is certainly useful there. However, when we get outside of hardware, the connection to the real world is a bit more nebulous.

One could argue within a business postal tracking application that a package is either delivered on-time or not. It cannot be "half" delivered on-time. At least we don't represent it that way. Quantum mechanics tells us that it may be impossible to measure the exact location of a given package while it is moving. We can measure a package's position at rest, but cannot accurately measure when it came to rest. Thus, in reality it is not Boolean. The Boolean-ness that we use is an abstraction that we as humans place over our view of the world. It is an imperfect abstraction, but good enough for most uses. It can in some ways be compared to Newtonian physics. Relatively and quantum physics have proven more accurate than Newtonian's abstractions. However, Newtonian physics has proven a "good enough" idiom for the vast majority of uses. BA may be the same: A useful lie.

There is kind of a chick-or-egg question about whether BA influenced our thinking by "leaking" into our education system, or the other way around. Some remote tribes have been found that don't appear to view time as linear. Our linear view may be shaped by our idioms, such as clocks and calendars. Boolean algebra may have done something similar directly or indirectly.

As we move up the "complexity ladder" of our idioms, say to Relational algebra, then a connection to the real world is even murkier. Many, including me, suggest relational makes a great building block, yet some would rather use OOP instead and do all processing and data manipulation through behavioral class interfaces only. (Some have argued that OO and relational are really orthogonal, but I don't agree. Perhaps that is a nice topic for another month.)

All that CS writing and very little of it gives any useful, objective tools to answer "which is better?". Because of the Turing Complete rule (described above), the predictive power is not an issue: they can all deliver the correct answer. It is like being a music or movie producer: suggested and recommended songs and plots are plentiful. Everybody and their dog gives you samples and ideas. The hard part is narrowing them down so that one is not overwhelmed with work and choices.

Even simple things are often hard to pin down. For example, nobody has objectively proven that structured programming is better than "GO TO" statements, yet very few programmers would want to return to goto's glory days. After many years of trying to discover a describable reason, I have concluded that my own distaste for goto's comes from lack of inter-developer consistency and lack of flow-control visual cues. Indentation of blocks supplies easier-to-digest visual information to the brain that goto code does not. (I was forced to use goto's soon out of college because the company did not want to pay for compiler upgrades, living with the older 60's version instead.)

But, I don't yet know how to measure "consistency" in flow-control. If it was an issue in a debate, I could not claim I have objective evidence. Maybe there are conventions and patterns to goto's that were simply not documented. And the second one, visual cues, relates to the psychology of perception. Maybe a fancy goto-based code editor could automatically draw lines between goto branch points to also give visual cues. Further, some have suggested that goto's have been partially resurrected in the form of the try/catch blocks that are currently in style in many languages. Thus, the goto idiom may not be totally gone after all. (I also tend to have some complaints about try/catch blocks and how they are used, but that is perhaps another month's topic.)

The bottom line is that as things now stand, Computer Science is not science and Software Engineering is not engineering.

Don't get me wrong, I am not suggesting those works are all useless. Not being science or engineering does not automatically make them useless. My complaint is mostly that they are mislabeled and that leaving them mislabeled is misleading and perhaps intellectually dishonest.

They are idioms, and idioms make good starting points, even if there are too many choices with no objective way to compare. Going back to our god analogy, without some other universes to compare to, it is hard to create a universe from scratch. I don't know of a lot of games that use non-Newtonian physics as their base (although they may occasionally violate or stretch Newton's laws for effect). Even though it may not be hard to imagine stuff way beyond what we are familiar with, it is harder to make it consistent and work together. An LSD trip may perhaps be cool (they say), but it is hard to get much useful work done there.

In some Star Trek episodes the orbiting ship was unable to communicate with the away team on the ground because of some "interference field" around the planet or landing area. However, the ship was still able to fire their giant "phaser" ray to the surface. If they are able to fire a ray, then they could in theory use the ray to send a Morse-code or binary message. For example, "SOS" in Morse would be "blast blast blast, blaaaaast blaaaaast blaaaaast, blast blast blast". Phaser pistol blasts from the surface should similarly show up on ship sensors.

Inconsistencies like this may not matter much on scripted TV, but if somebody creates a world with such contradictory rules and tries to "run" it, like one does with The Sims (but perhaps more advanced), then the inconsistencies start to bite. In the case of our Trek simulation, once characters learn they can send Morse-code with weapons, the plots might start to get boring and predictable. As a virtual god, we won't like the world that we created. It does not have the desired output (entertaining plots in this example).

If we don't want to wrestle with such issues, we might be better off using a known idiom, such as the American wild west, to avoid having unexpected results. The wild west was real and so we know that nothing really odd is going to happen as long as we stick to its rules. Neither the Cherokee nor cowboys are going to build an ultimate weapon and wipe out the continent in a big blast, for example (at least not for a few decades of simulated time).

This is similar to the purpose of the CS idioms we talked about. They have been road-tested. Any major inconsistencies or problems would have already been discovered by now, and smaller ones get incrementally fixed over time, or at least better understood so that workarounds are documented. Maybe there are revolutionary idioms that we have yet to discover that provide new and different ways to get the answer. But at this point we don't know and can't tell.

But "wait!" you say, "isn't road-testing a scientific process?" Outside of producing the correct output, most of the "testing" is still within our minds. Attempted objectivity solutions just keep bringing us back to our minds like an amusement park maze with no exits. We might perhaps be able to test that one has learned how to use an idiom to some extent, but that does not necessarily say if it is the best idiom let alone the best possible idiom.

If an idiom does not work out, we cannot tell if it is because the idiom user does not "get it", or some other issue. Not "getting it" may indicate either a bad user or a bad idiom. An idiom that requires knowledge of quantum physics just to add two numbers is probably not going to be very popular even if it produces the correct answer. Nor do we know if an apparently faulty idiom is repairable. Just because we don't know how to repair it does not necessarily mean it is absolutely not repairable. There may be an easy tweak that we just have yet to stumble upon. We just don't know what we don't know. This might seem like an obvious statement, but it has huge ramifications.

Using our Trek analogy, maybe there are ways to fix the Trek universe such that the away team really can be frequently cut off from the ship to keep the plots interesting. But the first tries will probably introduce other inconsistencies or unexpected side-effects. If you want a predictable or manageable virtual world, then it is easier to copy either familiar worlds or ideas from familiar worlds, such as the wild west. The devil you know is often better (or at least more comfortable) than the one you don't know. Those CS books are our known devils.

If one can perfect a new idiom by working out the bugs and oddities or better understanding it, then it can enter into the realm of useful idioms to choose from. It provides us with more options to use in our virtual worlds. Variety may also allow more people to participate in world-building because there is a greater chance that an idiom can be found to match a given person's personality and cognitive approach.

The GUI is an example of this. It allowed more people to learn and use computers because it presents a virtual interface comparable to physical buttons and physical paper forms and folders that most people are familiar with. GUI's are not necessary to have working software, and some could argue that they require more labor than a well-tuned character-based interface, but many people just voted them more comforting because GUI's simulate a familiar idiom. Who knows, maybe The Sims will someday replace GUI's as the preferred interface. Houses could be folders and people be files. I'm sure Sims fans can dream up all kinds of OS and office application analogies (and probably some cruel Microsoft jokes).

On the programming front, I have seen spreadsheet idioms allow accountants and clerks to become amateur programmers. Lotus 1-2-3 allowed one to use existing spreadsheet idioms of reference-able cells, menus, and sheets to form programming idioms. For example, instead of a go-to programming statement with labels, in Lotus you could use a cell reference instead. (I can't say I found their programs very maintainable myself, but it was still an interesting phenomenon.) Commands were mostly just menu keystrokes based on the character-based interface. Here is a rough example with altered syntax and command names for illustration purposes:

          |          A             |       B 
          |------------------------------------ ...
        1 | ifGoto(c5 > 7, a3)     |
        2 | menuKey("AXg6<enter>") |
        3 | set(c9, "45.0")        |
        4 | set(e7, c9)            |
        5 | ...etc...              |

Assuming the program starts at cell A1, this is how to interpret the example:
  • Cell A1 - If the value in cell c5 is greater than seven, then go to cell a3 (skipping over a2), else continue to next cell. (This is basically a Fortran-like IF...GOTO idiom.)

  • Cell A2 - Select item "A" from the menu, then "X" in the resulting submenu. Option "X" results in an input prompt to enter a cell reference, which is "g6" here. "<enter>" emulates the Enter key. Basically, the MenuKey function just sends virtual key-strokes as if you were typing those very same letters. You don't need separate functions for each command because they are already in the menu. A "recorder" feature could even record your keystrokes and create the string for you.

  • Cell A3 - This Set command simply sets the value of cell c9 to the value "45.0".

  • Cell A4 - Copy the value of cell c9 to e7.

This approach provides a Turing-complete language that has access to any cell and any feature found in the menus mostly by echoing how the user would do it manually. It also uses cells instead of variables so that the user does not have to learn about "variables" in the sense that "regular" programmers do. It is a grand example of concept reuse.

Microsoft's spreadsheet never shared the same ability among accountants and clerks because it used Visual Basic statements and variables instead of cells and menu keystroke echoing. In other words, it did not leverage ideas from the familiar world of manual spreadsheet usage.

Even though Microsoft Excel programming was less approachable, Microsoft still "won" the spreadsheet war for other reasons. First off, they were better priced at the time, partly because of product bundling deals. Second, they added more features that reduced the need for direct programming. This made the product more appealing to user who had no interest in programming. Third, Lotus was late transitioning from the character world to the GUI world. The simplicity of the "macro" learning depended heavily on character UI conventions. It is harder to represent mouse equivalents. Forth, Lotus tended to have annoying memory problems. But these did not matter much to Lotus experts. Once users learned macro programming they often didn't want to convert to Microsoft. However, they were usually outvoted.
One could perhaps argue that even though the spreadsheet idioms make learning programming easier for manual spreadsheet users, that by itself does not make the spreadsheet-centric programming approach "better", once learning curves are factored out. But everybody has a learning curve. It is probably a very rare programmer who can truly master all known paradigms in a life-time (or until they are forced into management). Sure, one can learn all major paradigms, but perfecting them all is a different matter. For the majority of programmers, it may make more economic sense to master one or few paradigms rather than learn many to a "good enough" level. In the end, our minds are still the primary factor to weigh. The mind is where both the power and limits of software are found, not the outside world.

See Also:
Goals and Metrics

© Copyright 2005 by Findy Services and B. Jacobs. All rights reserved.