# Principles

## First make it work. Then make it right. Then make it fast.

From [M06]:

"First make it work. Then make it right. Then make it fast." This quotation, often with slight variations, is widely known as "the golden rule of programming." As far as I've been able to ascertain, the quotation is by Kent Beck, who credits his father with it. Being widely known makes the principle no less important, particularly because it's more honored in the breach than in the observance. A negative form, slightly exaggerated for emphasis, is in a quotation by Don Knuth (who credits Hoare with it): "Premature optimization is the root of all evil in programming."

Optimization is premature if your code is not working yet, or if you're not sure about what, exactly, your code should be doing (since then you cannot be sure if it's working). First make it work. Optimization is also premature if your code is working but you are not satisfied with the overall architecture and design. Remedy structural flaws before worrying about optimization: first make it work, then make it right. These first two steps are not optional; working, well-architected code is always a must.

In contrast, you don't always need to make it fast. Benchmarks may show that your code's performance is already acceptable after the first two steps. When performance is not acceptable, profiling often shows that all performance issues are in a small part of the code, perhaps 10 to 20 percent of the code where your program spends 80 or 90 percent of the time. Such performance-crucial regions of your code are known as its bottlenecks, or hot spots. It's a waste of effort to optimize large portions of code that account for, say, 10 percent of your program's running time. Even if you made that part run 10 times as fast (a rare feat), your program's overall runtime would only decrease by 9 percent, a speedup no user would even notice. If optimization is needed, focus your efforts where they'll matter—on bottlenecks.

## Make Interfaces Easy to Use Correctly and Hard to Use Incorrectly

Scott Meyers in [H10]:

One of the most common tasks in software development is interface specification. Interfaces occur at the highest level of abstraction (user interfaces), at the lowest (function interfaces), and at levels in between (class interfaces, library interfaces, etc.). Regardless of whether you work with end users to specify how they'll interact with a system, collaborate with developers to specify an API, or declare functions private to a class, interface design is an important part of your job. If you do it well, your interfaces will be a pleasure to use and will boost others' productivity. If you do it poorly, your interfaces will be a source of frustration and errors.

Good interfaces are:

• Easy to use correctly

People using a well-designed interface almost always use the interface correctly, because that's the path of least resistance. In a GUI, they almost always click on the right icon, button, or menu entry, because it's the obvious and easy thing to do. In an API, they almost always pass the correct parameters with the correct values because that's what's most natural. With interfaces that are easy to use correctly, things just work.

• Hard to use incorrectly

Good interfaces anticipate mistakes people might make, and make them difficult—ideally, impossible—to commit. A GUI might disable or remove commands that make no sense in the current context, for example, or an API might eliminate argument-ordering problems by allowing parameters to be passed in any order.

A good way to design interfaces that are easy to use correctly is to exercise them before they exist. Mock up a GUI—possibly on a whiteboard or using index cards on a table—and play with it before any underlying code has been created. Write calls to an API before the functions have been declared. Walk through common use cases and specify how you want the interface to behave. What do you want to be able to click on? What do you want to be able to pass? Easy-to-use interfaces seem natural, because they let you do what you want to do. You're more likely to come up with such interfaces if you develop them from a user's point of view. (This perspective is one of the strengths of test-first programming.)

Making interfaces hard to use incorrectly requires two things. First, you must anticipate errors users might make and find ways to prevent them. Second, you must observe how an interface is misused during early release and modify the interface—yes, modify the interface!—to prevent such errors. The best way to prevent incorrect use is to make such use impossible. If users keep wanting to undo an irrevocable action, try to make the action revocable. If they keep passing the wrong value to an API, do your best to modify the API to take the values that users want to pass.

Above all, remember that interfaces exist for the convenience of their users, not their implementers.

## Keep it simple, stupid (KISS)

The acronym was reportedly coined by Kelly Johnson, lead engineer at the Lockheed Skunk Works (creators of the Lockheed U-2 and SR-71 Blackbird spy planes, among many others).

## The Unix philosophy

The "Unix philosophy" originated with Ken Thompson's early meditations on how to design a small but capable operating system with a clean service interface. It grew as the Unix culture learned things about how to get maximum leverage out of Thompson's design. It absorbed lessons from many sources along the way.

The Unix philosophy is not a formal design method. It wasn't handed down from the high fastness of theoretical computer science as a way to produce theoretically perfect software. Nor is it that perennial executive's mirage, some way to magically extract innovative but reliable software on too short a deadline from unmotivated, badly managed, and underpaid programmers.

The Unix philosophy (like successful folk traditions in other engineering disciplines) is bottom-up, not top-down. It is pragmatic and grounded in experience. It is not to be found in official methods and standards, but rather in the implicit half-reflexive knowledge, the expertise that the Unix culture transmits. It encourages a sense of proportion and skepticism—and shows both by having a sense of (often subversive) humor.

Doug McIlroy, the inventor of Unix pipes and one of the founders of the Unix tradition, had this to say at the time [McIlroy78]:

1. Make each program do one thing well. To do a new job, build afresh rather than complicate old programs by adding new features.
2. Expect the output of every program to become the input to another, as yet unknown, program. Don't clutter output with extraneous information. Avoid stringently columnar or binary input formats. Don't insist on interactive input.
3. Design and build software, even operating systems, to be tried early, ideally within weeks. Don't hesitate to throw away the clumsy parts and rebuild them.
4. Use tools in preference to unskilled help to lighten a programming task, even if you have to detour to build the tools and expect to throw some of them out after you've finished using them.

He later summarized it this way (quoted in A Quarter Century of Unix [Salus]):

This is the Unix philosophy: Write programs that do one thing and do it well. Write programs to work together. Write programs to handle text streams, because that is a universal interface.

Rob Pike, who became one of the great masters of C, offers a slightly different angle in Notes on C Programming [Pike]:

• Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so fon't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
• Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
• Rule 3. Fancy algorithms are slow when ${\displaystyle n}$ is small, and ${\displaystyle n}$ is usually small. Fancy algorithms have big constants. Until you know that ${\displaystyle n}$ is frequently going to be big, don't get fancy. (Even if ${\displaystyle n}$ does get big, use Rule 2 first.)
• Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
• Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
• Rule 6. There is no Rule 6.

Ken Thompson, the man who designed and implemented the first Unix, reinforced Pike's rule 4 with a gnomic maxim worthy of a Zen patriarch:

When in doubt, use brute force.

More of the Unix philosophy was implied not by what these elders said but by what they did and the example Unix itself set. Looking at the whole, we can abstract the following ideas:

1. Rule of Modularity: Write simple parts connected by clean interfaces.
2. Rule of Clarity: Clarity is better than cleverness.
3. Rule of Composition: Design programs to be connected to other programs.
4. Rule of Separation: Separate policy from mechanism; separate interfaces from engines.
5. Rule of Simplicity: Design for simplicity; add complexity only where you must.
6. Rule of Parsimony: Write a big program only when it is clear by demonstration that nothing else will do.
7. Rule of Transparency: Design for visibility to make inspection and debugging easier.
8. Rule of Robustness: Robustness is the child of transparency and simplicity.
9. Rule of Representation: Fold knowledge into data so program logic can be stupid and robust.
10. Rule of Least Surprise: In interface design, always do the least surprising thing.
11. Rule of Silence: When a program has nothing surprising to say, it should say nothing.
12. Rule of Repair: When you must fail, fail noisily and as soon as possible.
13. Rule of Economy: Programmer time is expensive; conserve it in preference to machine time.
14. Rule of Generation: Avoid hand-hacking; write programs to write programs when you can.
15. Rule of Optimization: Prototype before polishing. Get it working before you optimize it.
16. Rule of Diversity: Distrust all claims for "one true way".
17. Rule of Extensibility: Design for the future, because it will be here sooner than you think.

If you're new to Unix, these principles are worth some meditation. Software-engineering texts recommend most of them; but most other operating systems lack the right tools and traditions to turn them into practice, so most programmers can't apply them with any consistency. They come to accept blunt tools, bad designs, overwork, and bloated code as normal—and then wonder what Unix fans are so annoyed about.

## Easier to change (ETC)

From [TH20]:

Good Design Is Easier to Change Than Bad Design.

A thing is well designed if it adapts to the people who use it. For code, that means it must adapt by changing. So we believe in the ETC principle: Easier to Change. ETC. That's it.

As far as we can tell, every design principle out there is a special case of ETC.

Why is decoupling good? Because by isolating concerns we make each easier to change. ETC.

Why is the single responsibility principle useful? Because a change in requirements is mirrored by a change in just one module. ETC.

Why is naming important? Because good names make code easier to read, and you have to read it to change it. ETC!

ETC Is a Value, Not a Rule.

Values are things that help you make decisions: should I do this, or that? When it comes to thinking about software, ETC is a guide, helping you choose between paths. Just like all your other values, it should be floating just behind your conscious thought, subtly nudging you in the right direction.

## Don't Repeat Yourself (DRY)

From [TH20]:

We feel that the only way to develop software reliably, and to make our developments easier to understand and maintain, is to follow what we call the DRY principle:

Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

Why do we call it DRY?

DRY—Don't Repeat Yourself.

[...]

DRY Is More Than Code

[...]

DRY is about the duplication of knowledge, of intent. It's about expressing the same thing in two different places, possibly in two totally different ways.

Here's the acid test: when some single facet of the code has to change, do you find yourself making that change in multiple places, and in multiple different formats? Do you have to change code and documentation, or a database schema and a structure that holds it, or...? If so, your code isn't DRY.

## Bertrand Meyer's Five Criteria for modular design

From [M97]:

### Modular decomposability

A software construction method satisfies Modular Decomposability if it helps in the task of decomposing a software problem into a smaller number of less complex subproblems, connected by a simple structure, and independent enough to allow further work to proceed separately on each of them.

### Modular composability

A method satisfies Modular Composability if it favors the production of software elements which may then be freely combined with each other to produce new systems, possibly in an environment quite different from the one in which they were initially developed.

### Modular understandability

A method favors Modular Understandability if it helps produce software in which a human reader can understand each module without having to know the others, or, at worst, by having to examine only a few of the others.

### Modular continuity

A method satisfies Modular Continuity if, in the software architectures that it yields, a small change in a problem specification will trigger a change of just one module, or a small number of modules.

### Modular protection

A method satisfies Modular Protection if it yields architectures in which the effect of an abnormal condition occurring at run time in a module will remain confined to that module, or at worst will only propagate to a few neighbouring modules.

## Bertrand Meyer's Five Rules which we must observe to ensure modularity

From [M97]:

### Direct Mapping

The modular structure devised in the process of building a software system should remain compatible with any modular structure devised in the process of modeling the problem domain.

### Few Interfaces

Every module should communicate with as few others as possible.

### Small Interfaces (weak coupling)

If two modules communicate, they should exchange as little information as possible.

### Explicit Interfaces

Whenever two modules ${\displaystyle A}$ and ${\displaystyle B}$ communicate, this must be obvious from the text of ${\displaystyle A}$ or ${\displaystyle B}$ or both.

### Information Hiding

The designer of every module must select a subset of the module's properties as the official information about the module, to be made available to authors of client modules.

## Bertrand Meyer's Five Principles of software construction

From [M97]:

### Linguistic Modular Units principle

Modules must correspond to syntactic units in the language used.

### Self-Documentation principle

The designer of a module should strive to make all information about the module part of the module itself.

### Uniform Access principle

All services offered by a module should be available through a uniform notation, which does not betray whether they are implemented through storage or through computation.

### Open-Closed principle

Modules should be both open and closed.

### The Single Choice principle

Whenever a software system must support a set of alternatives, one and only one module in the system should know their exhaustive list.

# Bibliography

• [H10] Kevin Henney. 97 Things Every Programmer Should Know. O'Reilly, 2010.
• [M06] Alex Martelli. Python in a Nutshell, second edition. O'Reilly, 2006.
• [M97] Bertrand Meyer. Object-Oriented Software Construction, second edition. Prentice Hall, 1997.
• [TH20] David Thomas, Andrew Hunt. The Pragmatic Programmer, 20th Anniversary Edition. Addison–Wesley, 2020.