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Fast Branchless Integral Axis-Aligned Bounding Box Intersection Test


If you're like me you have a hard time finding basic things on the Internet.  Maybe these concepts aren't all that basic then, I haven't really considered that.  If you're also like me you want to see the code, not endless walls of text, so without further ado, here is the fast code to detect if two integral 3-dimensional axis-aligned bounding boxes intersect without branching assuming a twos-complement architecture:

    boolean intersects(IntAxisAlignedBox left, IntAxisAlignedBox right) {
lineDeltaFactor(left.min.x, left.max.x, right.min.x, right.max.x) |
lineDeltaFactor(left.min.y, left.max.y, right.min.y, right.max.y) |
lineDeltaFactor(left.min.z, left.max.z, right.min.z, right.max.z)
) == 0;

int lineDeltaFactor(int leftMin, int leftMax, int rightMin, int rightMax) {
final int
leftWidth = leftMax - leftMin,
rightWidth = rightMax - rightMin,

leftMid = leftMin + ((leftMax - leftMin) >> 1),
rightMid = rightMin + ((rightMax - rightMin) >> 1);

return (abs(leftMid - rightMid) << 1) / (leftWidth + rightWidth + 1);

int abs(int value) {
final int mask = value >> (Integer.SIZE - 1);

value ^= mask;
value += mask & 1;
return value;

Now, if you have a smart compiler / LLVM, it will inline expand these functions to avoid expensive stack juggling and v-table lookups. Note that this example also includes a branchless version of the absolute value function.  Now if your architecture isn't twos-complement, then this isn't going to work because abs will return garbage. This will fail for input values that are close to 32-bit extremes (i.e. Integer.MAX_VALUE and Integer.MIN_VALUE).

Computing n-tuple Combinations of a Set - Java



I recently ran into a problem while building the type compiler for my proprietary scripting language.  It seemed like a braindead problem I should have remembered from my high-school days of coding.  After reviewing a few recursive and somewhat clumsy examples on the Internet, and also after decidedly turning down Guava (In interest of compactness, external dependencies on my compiler are kept at a minimum), I decided to attack the problem on my own.


Calculate the complete list of n-tuple combinations of elements in a set of arbitrary type.  For example, computing the triple (3-tuple) list of combinations of the following set should satisfy the following:

Input: [1, 2, 3, 4, 5]
Output: [[1,2,3],[1,2,4],[1,2,5],[1,3,4],[1,3,5],[1,4,5],[2,3,4],[2,3,5],[2,4,5],[3,4,5]]


I came up with a solution that boasts the following features:

  1. Iterable (no memory consumption)
  2. Non-recursive (no stack juggling)
  3. n-tuple combinations only (as opposed to permutations)


Here's the code:

package com.extollit.collect;

import java.text.MessageFormat;
import java.util.*;

public class CombinationsIterable<T> implements Iterable<List<T>> {
private final List<T> delegate;
private final int rank;

* Create an iterable list over the specified list that returns an iterator for iterating over
* all the n-tuple combinations in the list (n-tuple specified by the rank parameter).
* @param delegate The list to compute n-tuple sub-set combinations from
* @param rank The size of the combination tuple, must be > 0 and <= delegate size
public CombinationsIterable(List<T> delegate, int rank) {
this.delegate = delegate;
if (rank > delegate.size() || rank <= 0)
throw new IllegalArgumentException(
MessageFormat.format("Combination tuple size (rank) must be less than or equal to the delegate list size and greater than zero, but {0} <= 0 | > {1}", rank, delegate.size())

this.rank = rank;

public Iterator<List<T>> iterator() {
return new ComboIter();

private class ComboIter implements Iterator<List<T>> {
private final ArrayList<T> workspace = new ArrayList<T>(rank);
private final int [] indexStack;
private final int spanSize;

private int stackPointer;
private List<T> current = null;

private ComboIter() {
indexStack = new int[rank];
stackPointer = 0;
indexStack[0] = -1;
spanSize = delegate.size() - indexStack.length;
for (int c = 0; c < indexStack.length; ++c)
workspace.add(c, null);

private List<T> findNext() {
while (stackPointer >= 0 && ++indexStack[stackPointer] > spanSize + stackPointer) {

if (stackPointer >= 0) {
this.workspace.set(stackPointer, delegate.get(indexStack[stackPointer]));
for (int c = stackPointer + 1; c < indexStack.length; ++c) {
indexStack[c] = indexStack[c - 1] + 1;
this.workspace.set(c, delegate.get(indexStack[c]));
return this.workspace;
} else
return null;

public boolean hasNext() {
return acquireCurrent() != null;

public List<T> next() {
List<T> current = acquireCurrent();

if (current != null) {
current = new ArrayList<T>(current);
this.current = findNext();

return current;

private List<T> acquireCurrent() {
if (this.current == null)
this.current = findNext();
return this.current;

public void remove() {
throw new UnsupportedOperationException(
"Combinations iterator does not support removal of combinations because it's functional mapping to the underlying collection is non-injective"

It passes a few of my tests, please notify me of any errors.

Why Automated Tests Suck


...or how to build a test plan that doesn't kill fairies.  This post discusses a few core automated test plan guidelines.  Bad tests can actually defeat the purpose of automated tests entirely and really bad tests make manual testing a better option (wat?).

Noise vs. Signal

Let's take a moment to compare a test plan to something that would make Carl Sagan feel like a kid in a candy store: exoplanet research!  Scientists often search for exoplanets by seeking-out an oscillating signal amid a great deal of noise.  Essentially you're looking for evidence of a wobble in the star's movement because of a planet's (or a borg transwarp nexus') gravity is tugging on it as it revolves around its star.  But at such great distances away such a signal is encumbered by a myriad of problems.  The signal to noise ratio for this analysis is less than 1%.  Finding genuine planets is incredibly difficult and false positives are common (Zarmina).  What's my point?  The more noise, the harder it is to isolate a genuine signal.  Similarly, if you have tests that fail (and it's ok, those failures don't really count) then it becomes much harder to isolate genuine test failures from test failures that "don't matter."  Particularly for a release test plan, your release test plan should have zero failures and zero skipped tests.  The more you skip tests or ignore tests, the more you erode its utility and reliability as a device that produces a definitive and objective result.

I'm a New Hire Idiot

Another reason to ensure that your release test plan has absolutely no failed or skipped tests is because your test plan should be self-evident and be independent of idiomatic understanding.  For example you should never have to say that "test x is failing because we haven't updated the x87g filter data model for the cogsworth subsystem metacontroller yet, just ignore it because I don't think anybody uses that component anyway."  This is an example of idiomatic understanding and causes the naive person to lose trust in your test plan (and pull their hair out).  When new hires join your organization (or transfers from another team) often ramping-up on idiomatic understanding is so difficult because it is fragmented, subjective, inconsistent, and exemplary of the human vices that lead to conflicts.  Make tests a priori in their function and purpose.

Tests as Strong as Bismuth!

Tests should not be so brittle that you're spending more time updating your test data than you are writing code that brings value to your users.  In fact, when this is true often the tests are abandoned and left failing and then ignored and then forgotten and yet they still linger and co-ops ask about them and get an earful about the myth of the one test and the war of the first build, but it was all for naught because Isildur was struck down by a brittle test; volatile data pierced his heart.  And yet no co-op has been found worthy to take the one test deep into JMeter and destroy its design once and for all.

One might think that tests like these ought to be removed, but it isn't clear to the average person whether that would do more harm than good.  If a test fails too often then one of two things has to happen: either you update the test, or you completely redesign the test.  Removal of a test is quite simply a step backward.  Someone saw fit to provide that coverage initially, so upon what basis does one reason that such coverage is no longer necessary?  One side-effect of test removal is that can signal to the naive person that the associated features have also been removed (then you get a call from an angry customer threatening to sue your company for breach of contract when the feature broke and was never fixed, well, more often they will simply move-on and invest their dollars elsewhere silently.  The sad thing is this will happen too with brittle constantly failing tests that go ignored).

Sometimes a test that fails too often benefits from a complete overhaul because quite likely, and I'll put it bluntly, your test is just wrong.  A test that isn't self-contained is an integration test.  Some believe integration tests are as scam, I believe they are unavoidable.  That isn't necessarily a mutually exclusive statement either come to think of it.  However, if your unit test doesn't need to consume external resources (of any kind) then ensure it doesn't.  In fact, you may want to redesign the code the test is built around. "But wait Batman!  This is looking a lot like TDD!"  You're right, it is.

Tests Express Simple Statements

Each individual test should make a specific statement that can be used to ensure a straightforward objective expectation is aligned with actual state.  Here are some examples:
  1. When I call the delete function passing existing user x to it, it should yield the HTTP 200 success response.
  2. If I call the delete function passing a non-existent user x to it, it should yield the expected HTTP 404 response.
  3. When I call the get model foo function, the response body should contain a JSON object containing at least all of the following field names.
Avoid writing tests that involve too many test conditions and assertions.  Otherwise, it starts to take on the stench of the integrated test.  This may manifest as either multiple assertion statements or for example the exclusive comparison of a result against a template.  These are both examples of testing too many conditions in one test and does not resolve to a clear statement.  For example, compare the following two statements:
  1. The result should contain the following fields in this precise order and contains nothing else besides those fields and have values match these other values precisely.
  2. The result should contain at least the following fields.
Number two is a good definition for a test whereas number one can be broken-down into 4 separate statements (and consequently 4 separate tests).  Tests that test multiple conditions are often duplicating those conditions in other tests, and when they fail it simply adds more noise that developers and QA have to sift through like a child drowning in a playpen full of plastic balls.  Test plan noise erodes usability, refined targeted tests that reflect clear and concise statements increase clarity and usability.  If you cannot express your unit test in one coherent sentence, then it's not a unit test.

It's Not My Fault! The Server Was Down

This essentially embodies the difference between a unit test and an integration test.  However, I want to attack this issue from an angle independent of vernacular.  Tests should be consistent in terms of the environment and resources upon which they depend.  The other dimension to consider besides space is time.  A good way to rattle a developer's nerves is to introduce a test that downloads JSON from some bizarre and esoteric web service in China on Wednesdays unless it's a full-moon with the exception of when Mars is at apposition precisely 39.2 hours before daylight savings.  This is a hyperbole, I don't think anyone would actually write a test like this (knock on wood), but it illustrates a scoping problem concerning time and space (e.g. China doesn't even use DST).  A reliable, self-contained, test without side-effects should not be contingent upon transient state such as specific dates, times and system locale.  With respect to the environment, all of the above can break, and it's never clear when that occurs.  A machine's internal clock can get thrown out of whack, or the system locale may be subtly different (e.g. en_CA vs en_US).

If you must test a system that relies upon something non-deterministic such as one of the following:
  1. The current time
  2. A random number
Then design TimeProvider and RandomNumberProvider interfaces and build them into your code.  In your unit test, you can fulfil these interfaces with implementations that are deterministic.

The environment and resources for which a test plan requires should be reproducible upon one machine and one machine only.  If it is absolutely necessary to introduce external dependencies then categorize your tests (discussed later).  Furthermore, tests should never assume locale or timings, you are not the master of the universe, He-Man beat you to the punch decades ago; or a century ago if this blog is still around eighty some-odd years from when it was written (that's good for a laugh); or I suppose any x number of years ago from now (your now, not my now) since it's impossible for me to predict when this post will be read (...or how a crazy person made a point by stumbling into his own dog food, tune in at 6pm tonight for the full story).  Here are some examples of unhelpful failures of tests using such kinds of resources:
  1. An authentication token used in requests expires on a specific date
  2. A DNS failure occurs for a host
  3. A static IP was changed for a host
  4. A host was down
  5. Matching localized error messages against a template
  6. Testing values from a database maintained by another team / department
  7. Using sleep to wait for some resource to become available
And suggested resolutions for these conditions:
  1. Generate a token as part of the test setup phase, destroy it as part of teardown.
  2. Use multiple DNS otherwise see #4.
  3. Never ever rely on static IPs, they're not even descriptive and you don't have a monopoly on what IT does with the infrastructure.
  4. Rather than fail the test, flag it as skipped, categorize this test elsewhere.
  5. Compare error codes, not messages
  6. Have your own copy of the database, update the database in tandem with the tests.  Consider the database as part of the coverage.
  7. Either poll or block, but don't sleep because it's not reliable to predict how much time a resource will require.  Apply a timeout, and if it elapses then flag the test as skipped, categorize this test elsewhere.
In general a test contingent upon external resources that become unreachable from time to time, if you must write such tests, they should not fail when the external resources become unavailable, rather they should be flagged as skipped. i.e. employ trinary state: pass, fail, skipped.

One may argue that it is sufficient to run the tests again if they fail because they often succeed eventually.  This is essentially saying that it's okay that these features don't always work, so don't worry if it blows-up, just keep hitting refresh until something shows-up.  Side-effects are ok, but side-effects in unit tests are just plain evil.  Don't go there.  Depending on your organization a test suite can take anywhere from half an hour to more than a day (maybe even more?).  If you expect people to simply re-run the test suite to get tests to pass, you're asking them, in the worst case scenario, to invest another day particularly if your tests aren't entirely stateless.

Now for the elephant in the room.  Ideally your tests are isolated and self-contained and have no reliance on external resources.  You might then shoot back with "But how are we supposed to test our REST API then without relying on external resources?"  You're asking the wrong question.  Solid tests are the product of a project designed from the ground-up that ritually employs Test-Driven Development.  If you find yourself in a situation where you have to employ integration tests to provide any coverage to some sub-system, then this is an example of code that is simply not testable (as far as TDD is concerned).

Categorize Your Tests

This is more of a suggestion than a rule of thumb.  TDD is not an exact science, in fact software engineering as a whole is an inexact science.  If you must have tests contingent upon transient, temporal or volatile state, then divide your test plan into categories characterized by the type of test:
  • Self-contained tests
  • Tests that interact with external resources
  • Tests involving temporal state
  • Tests that cannot be run concurrently with others
Additional to categorizing tests, thoroughly document any unreliable characteristics of the test so that the naive person will understand better what's happening when it is mysteriously skipped.  All test plan categories should be stored within the same test plan.  In other words, when someone opens the test-plan, all tests should be visible or at least evident and no tests should be hidden away in some obscure folder of the file-system.  Each category should also have a description.

Build a House then Tear it Down

Good tests should be stateless, idempotent, without side-effects.  Running one test should not subsequently affect the result of another test.  It should be possible to run any number of tests, cherry-picked or otherwise, in any order with consistent results each time.  Each and ever test should have its own setup and teardown.  Each test should setup the state it requires and teardown that state afterward leaving nothing behind that wasn't there initially and restoring anything that was there initially.  This may dramatically increase the time to execute a test plan, however a high-performance set of tests is not conducive to the fundamental purpose for a test plan.  Troubleshooting tests whose results appear to vary with each other is notoriously difficult particularly if such variance is not reflected in the purpose of the test.

Everyone Loves Minions

...or many threads to do your bidding.  Design your tests in such a manner that they can be forked and run concurrently.  The reason for this is to improve the performance of your test plan and make it scale so that you can simply farm it out to as many machines as you need to reduce that day-long test suite execution to just under an hour.  Another notable benefit of such a design depends largely on your product, but particularly for service-oriented software, concurrency is a critical factor of realistic use cases.

Here are some good design principles for a test plan that scales:

Remember Test-Driven Development?

Before you sit down to write a feature consider that it should be testable.  Think about testable code with respect to tests that can scale.  Can your code reliably service multiple requests concurrently without race conditions?  If you find it is too difficult to write tests that can be executed concurrently without randomly failing then it's likely not the test rather the feature design that's implicated.  A cornerstone of TDD is that asking questions about what you expect a sub-system to do helps influence good interface design.  This is why it's important to think about the test before implementing the feature.  The feature and its tests (the tests and its feature?) have an inextricable relationship.

Unique IDs

When you establish state for your test, or more simply put, creating records during setup, then create your entities with unique names keyed by the test name and current thread executing in the test plan.  This should be little more complex than using a name and a numeric sequence.  A numeric sequence to represent thread is more helpful than some ubiquitous unique ID because for example in the case of a race condition it can hint at the point of failure during thread interleaving.  For example, if you're testing deleting users, and the user name is the primary key, come-up with a username like "bob.smith-deletetest-thread-3."  When you tear down your state, rather than track the entities created during setup, remove all entities that match the template used to generate them (e.g. remove all "bob.smith-deletetest-thread-< any number >" entities).  This way if there are side-effects from a previous execution of the test, this will account for them.  It is also good practice to perform this before setup as well for it is reasonable to assume that the test owns any state keyed by a specific pattern that the test uses during setup especially if the name of the test is part of that key pattern.  Here is some example code:

function cleanup()
    var objects = service.getUsers();
    var re = /^bobsmith-deletetest-thread-\d+$/;
    for (var c = 0; c < objects.length; ++c)
        if (re.test(objects[c].name))
function setup()
        name: 'bobsmith-deletetest-thread-' + threading.getCurrentThreadNum()
function teardown()

Some tests by their nature imply a has before relationship, for example the "test global lockout policy" test that locks out all users for security reasons would probably cause all other tests to fail too.  It may be prudent to separate these tests into yet another isolated category so that the distinction in test characteristics is clear.

Last Word

In this post I discussed how test noise can erode the usability and reliability of a test plan.  Eventually people will learn not to trust what the tests are telling them and bugs are permitted to fly beneath the radar.  I also discussed how this is related to idiomatic understanding within the scope of the company, team, or even individual responsible for a particular test, and how this leads to conflicts and misunderstandings and broken builds and weeping release managers.  Tests should be focused and targeted and not try to account for a lot of different conditions.  Tests that involve external dependencies or temporal, transient and volatile resources are also more brittle than tests with a more self-contained environment.  I only advocate such tests if TDD is impossible due to legacy code that cannot be tenably refactored.  Categorize tests based on their nature so that if you must have tests more brittle than others, at least it's clear which ones are more brittle than others and document the why and the how (to resolve).  Tests should be tasteless, I mean stateless (it's lunchtime and I skipped breakfast).  Where possible, build tests that can be executed concurrently to improve performance and ensure that your features can handle concurrency well.

Mars Tycoon, The "Sandbox World" Strategy Game


extollIT Enterprises is developing a new kind of innovative video game. We call this genre the Sandbox World game. There are three other games I know of that are similar to our concept, but only one can be credited with its inspiration:
  • Dwarf Fortress
  • Infiniminer
  • Minecraft
  • Roblox
If you haven't heard of these games, I suggest trying Minecraft or Roblox. Dwarf Fortress has inspired Mars Tycoon, but it is very difficult to get into if you are a novice gamer.

Our small team of three has been developing this game since the fall of 2008. You are a space-pioneer in the year 2492 and desire to escape the chaotic political climate of Earth to investigate a strange new mineral on Mars (and beyond) first discovered on a small asteroid that impacted Earth. Scientists have dubbed it with the name Loganite and although it's chemical signature has long been familiar to scientists it has never been found in nature let alone in vast quantities.

Loganite has significant commercial value as a nuclear fuel as well as having other peculiar benefits. Scientists are only just beginning to discover Loganite's value to mankind.

Your job is to establish a mining base on Mars, extract the Loganite, and compete against other nations scrambling for the mineral. You will have to defend your mining base from saboteurs, natural disasters, and skirmish attacks.

Launch of Mars Tycoon is scheduled for 2015. If you posses any of the following skills / experience / knowledge and would be interested in joining our team, please contact us using the About -> Contact Us menu at the top of the page:
  • 3D Modeling / Animation
  • 3D Programming Experience
  • Programming for CG, CUDA, PhysX
  • AI Programming Experience
  • Biochemical Engineering
  • Mechanical Engineering
  • Civil Engineering
  • Chemical Engineering
  • Geophysics
  • Horticulture
  • Astrobiology
  • Genetic Engineering
"Wait a sec! These are pretty steep qualifications!"
Actually not really. Although some of these "qualifications" demand post-doctoral work, you don't even need to have a post-secondary degree here. Self-directed study is very acceptable. For example, we are interested in people with Chemical Engineering knowledge to help us design a realistic Asimovian Sci-Fi experience.

We have a rich and comprehensive vision for Mars Tycoon in terms of both storyline and gameplay mechanics, so Mars Tycoon will be an introductory game into this universe. If it is successful, we plan to release sequels and prequels each sporting both an expanded storyline and new gameplay mechanics.

Computing Arc-Length of a Spline - Advanced


Here is a snippet of my own software used in another project that computes the arc-length of a spline using the Gauss-Legendre quadrature (numeric method of integration) implemented with recursive template meta-programming in C++:

First-off, you will need a constant 5x2 array of doubles to initialize the abscissae for, which is essentially a pre-computed table of the roots of the Legendre polynomials of order n. In this example I use order of 5, which was accurate enough for my purposes:
 const double ABSCISSAE_5[5][2] = {
0.90617984593866399280, 0.23692688505618908751,
0.53846931010568309104, 0.47862867049936646804,
, 0.56888888888888888889,
0.53846931010568309104, 0.47862867049936646804,
0.90617984593866399280, 0.23692688505618908751

Stub out an exception to generate runtime errors when incorrectly implementing the class:
 class CalcEx : public std::exception {};

Below the recursive class template is defined. It uses template-inheritance and since templates are interface-agnostic, you will need to define this method on your class: inline real f (const real t) const. It's essentially providing the implementation for an abstract method declared in the base class.
 template <typename T, typename Derived, int NN>

<int N> T abscissa (const unsigned int i, const unsigned int j) const

CalcEx ();

<> T abscissa <5> (const unsigned int i, const unsigned int j) const

static_cast <T> (ABSCISSAE_5[i][j]);

<int N> T summate (const T val, const T a, const T b) const

val + summate <N - 1> (
abscissa <NN> (N - 1, 1) *
<const Derived *> (this) -> f(
b + a) / 2 +
b - a) / 2) * abscissa <NN> (N - 1, 0)

a, b

<> T summate <0> (const T val, const T a, const T b) const { return val; }
T compute (const T a, const T b) const

((b - a) / 2) * summate <NN> (0, a, b);

Now it's time to put our algorithm to use and build a class that takes a spline object (usually four points or two points and two tangents) and computes the arc-length using the Gauss-Legendre algorithm we've prepared:
template <typename Spline, typename real>
ArcLength : private GaussLegendreInt <real, ArcLength <Spline, real>, 5>

Spline & _spline;

ArcLength(const Spline & spline)
_spline(spline) {}

real f (const real t) const

MAGNITUDE(_spline.computeAt (t));

static inline
real calculate (const Spline & spline)

ArcLength(spline).compute(0, 1);

The spline object assumes a function called computeAt that returns a vector (2D, 3D or whatever) of which the function MAGNITUDE computes the vector magnitude of. You will define these yourself including your implementation of splines. There are plenty of examples on the Internet on how to implement splines. Some examples include Cubic B-Splines, Catmull-Rom Splines, and NURBS.

Innovation 101 for the Perfectionist


To innovate is to introduce something new to the public that is usable and meets a perceived need. This article will dispel some common misconceptions that the perfectionist may have with his approach to design. Since women are perfect anyway and have this area covered, I will only use masculine pronouns. Actually it's for the sake of brevity. As someone who IS a perfectionist, this article is written for other perfectionists just getting his or her feet wet with this stuff. I suppose you could say that a perfect design goes hand-in-hand with proven innovation. But as the perfectionist I cannot say I've always shared this perspective and it's difficult to get over it. Hopefully this article can dispel some of the myths holding the typical perfectionist back from greater success.

MYTH: Perfection in design is necessary.

The fundamental reason perfectionism fails as a good approach to software design is because perfectionism does not factor human error into code design. Human error is inevitable and "nobody's perfect". To expect consistent and perpetual perfection from yourself, your team, or even your customer's requirements is unrealistic. Therefore, the perfectionist must accept human error as a valid and acceptable factor in the design process. It is haphazard to turn a blind-eye to this variable. Expect loose-ends to come from anywhere. Probably the most dangerous assumption to make is that the customer has a good understanding of his or her business challenge. In the end trial and error is inevitable to evolve and refine a product.

MYTH: Perfectly designed software leads to a quality product

How do you measure the quality of a product? Simply by how accurately it accomplishes its original purpose, which is defined by the customer's requirements. But how often are those requirements written in stone? How often can we say that they everyone understands those requirements perfectly? The perfectionist operates with these assumptions. Furthermore, it takes too long to develop a product that is perfect. Since software requirements change quite frequently, the result can be a lot of wasted time on a polished but irrelevant design. If you can't get it out the door, if it's incomplete, then it is far from perfect. One key characteristic shared by all of the most successful products out there is that they each do exactly one thing very very well and that has been attained through multiple iterations of trial and error.

MYTH: A perfectly designed product is highly maintainable.

Since details of implementation are often up to one individual programmer, his perception of perfection may be way-off the mark to someone else. Furthermore, code that conforms to standard OO design patterns religiously is not maintainable because it proves to be an overwhelming task to trace program flow through a deluge of, however highly cohesive and decoupled, unfamiliar classes. An experienced developer knows that any algorithm that can fit conveniently into various design patterns is common-place and more than likely already in a reusable framework somewhere anyhow. For the rest of your intellectual property, you yourself know that it doesn't fit conveniently into standard design patterns. Moreover, you know that your product isn't innovative because it is common-place. And so it becomes necessary to bend the rules while ironically keeping in mind other developers who might have to look at it later.

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