Code is Poetry : 5 steps to bulletproof code

I recently read a Quora post about a meticulous cleaner who would not let the smallest blemish evade him – he’ll stop, fix the flaw and then step back to admire his work. The narrator admitted that the cleaner’s platform was one of the neatest he had ever seen around the world.

Morale of the story? The same principles apply to software development – programmers have to love their craft and put their best into making it stand out. Going the extra mile in simplifying complex code, removing dead code or writing unit tests help to increase the life of a code base. These actions might appear to impede development speed but again, what is the definition of slow? As a former colleague used to say: “we sometimes have to slow down to go faster”.

Poorly written software always comes back to haunt developers in unimaginable ways; such code will effortlessly conjure monster bugs of all types (e.g. heisenbugs, mutatorbugs, linkerbugs and more). Removing bugs in bad software is also a nightmare as fixes might spawn even more abominable bug monstrosities (without unit tests, how do you verify? ). Which would you prefer? Disappointed customers, having to work weekends and eventually rewriting the same code versus doing it well upfront. The latter does sound faster to me…

Enough ranting, how about a couple of suggestions on how to write bulletproof code?

1. Know what you’re doing

Have you ever had to work on impossibly cryptic code? To all programmers who disparage others who do not get ‘smart’ code, here is a beautiful quote from E.F.Schumacher:

“Any intelligent fool can make things bigger and more complex… It takes a touch of genius – and a lot of courage to move in the opposite direction.”

Most ‘smart’ code pieces are indicative of shallow problem domain knowledge. Most times, these pieces can be simplified by taking away pieces until the problem the author was trying to solve becomes clear.

The  first thing you want to do when approaching a problem is to stop and think deeply. Stop! Don’t even start that IDE!! Do you understand the problem thoroughly? Do you know what issues might arise? How about testing and completion criteria? Once brainstorming is done, then find the simplest, fastest and most effective way to clearly convey your solution. Always remember that programming languages are vehicles for expressing your thoughts.

Without fully understanding the task, it is possible to end up with over-complicated code or even worse: ‘smart’ code. Please do not create breeding grounds for evil bugs – developers have better things to do with productive hours. Simplicity is the true smartness…

2. State modification is the root of all most evil

Functional programming purists tout FP as being less susceptible to bugs because it avoids modifying state. The best functions are like Mathematical functions, e.g y = x². Why are they good? Well they always give the same results for the same input and have no extra hidden internal state lying around.

I like using functions a lot; composing them into larger pieces and building up systems based on them. Such systems rely heavily on piping – function outputs are passed to other functions as inputs. Clearly designed functions are great as they communicate intent (e.g. square vs x²), make it easy to follow the author’s thought process and provide simplifying abstractions.

Watch out for the double-edged sword of modifying state in functions, it is the root of most evil bugs. Having the wrong assumptions about state changes, obscure manipulations (i.e. inadvertently modifying loop invariants in functions) and improper naming inevitably lead to trouble.

Whenever possible, use a functional programming style that relies on stateless functions. Hopefully this saves you a lot of debugging pain – you can thank me later…

3. Clean up as you go

What is your typical reaction when you come across horrible code? Do you sidestep the mess (I didn’t write it…) or do you attempt to fix it?

Just as people are more likely to drop trash on a dirty sidewalk compared to a clean one, developers are also more likely to write horrible code if the original source code is bad. This attitude is one to avoid at all costs; if you can’t invest in cleaning up the dirt, at least don’t add to it! Remember the boy scouts’ rule – always leave the park better than you found it.

Ideally you should clean up the code as you work in it – it shows that you care and helps prolong the life of the code base. If you need to strike a balance between adding new features and fixing technical debt (which might not bring new business value), then carve out a small block of time (e.g. 30 minutes).

If you want to know why this is important, then you should read about the broken windows theory (not yet, finish the post).

4. Love your craft

The best craftsmen are immensely proud of their work and achievements; they try to show it off to everyone and won’t stop talking about it (even if we do not want to hear about it ).

You should be proud of your code. Seek feedback; what looks beautiful to you might be terrible code – perspective, as they say, is subjective. Don’t get defensive and hurt over review feedback too – it’s the code dude! Not you!! So swallow your pride, learn and improve the code. Growth involves some pain.

Spend the time to think about elegance, simplicity and brilliance; the extra time and effort pays off. Which would you prefer of the two outcomes?

  • Creating a masterpiece in 6 hours
  • Creating a ‘hacker-piece’ in 2 hours and then spending 10 hours in maintenance (bug fixes, extensions and changes)

It’s a trade-off; I don’t like hunting down bugs – I do it if I have to but I’d rather not…

5. Break your software yourself

There was a dramatic moment during the début of the Microsoft Surface – the presenter dropped it (intentionally) and everyone gasped! Why would anyone want to drop a tablet? There are countless woeful tales of smashed screens, internal damage and memory loss. However, the presenter knew the product’s limits and knew he could do that. Can you break your own software? Do you know its limits?

Go ahead! Break it!! How else would you know its limits? Nearly every piece of code can be broken – a function that calculates 1 + 1 will give a wrong result on a computer with only 1-bit of memory (yeah, I wonder how you’ll even get such a computer). 

It’s a shame if customers find obvious bugs that should never have gotten into production. Customers who run into show-stopper bugs are typically not happy and unhappy customers can consider alternatives. Broken software can cause huge losses (goodwill, money, credibility, tarnished brand images etc) so you do not want to take this lightly – unless you fancy going out of business.

In these days of web applications, your customers will do a lot of testing for you – the more users you have the more weird scenarios people would put your software through. Do not worry about the extreme cases, the normal expected way has to work well though.

So try to break your code yourself before you call it done. This makes it more robust and you get to know of the corner cases. Even if you do not fix them, such knowledge enables you to handle customer complaints.

Inshaaha Allaah I hope to write about how to break software some time soon.

Now go and write some better code…


How to watch variables in JavaScript

We all have to track variables while debugging; generally the easier it is to monitor changes, the faster bugs can be detected and fixed.

Web developer tools expose various methods for tracking changes in variable values. There are a couple of drawbacks e.g. non-uniform support across platforms) but again, half-bread is better than none :).

Curious about such methods? Let’s take a walk…

1. Chrome’s Object.Observe

First on the list is Chrome’s Object.observe static method. The observe/unobserve functions are static functions – they can only be called on Object and do not exist on object instances. Lets take an example:

var obj = {};
Object.observe(obj, function(changes) {

obj.val = "Hello"

// [{  name: "val",
//     object: { val: "Hello" },
//     type: "add" }]

That was pretty cool right? How about observing primitives?

var val = 2;

Object.observe(val, function(changes) {

//TypeError: Object.observe cannot
//observe non-object

Disappointed? Maybe we should check out Firefox’s watch function then…

TIP: Chrome also has experimental Array.observe and Array.unobserve methods. Don’t use in production!

2. Firefox’s method

Firefox differs from Chrome because it exists on object instances; thus any object can watch for changes to its inner variables. Confused? Lets see an example:

window.val = 2;
//equal to var val = 2;"val",function(id, old, cur) {
 console.log("Changed property: ", id);
 console.log("Original val: ", old);
 console.log("New val: ", cur);

val = 4;

// Changed property: val
// Original val: 2
// New val: 4

Advantages? Well, you can now watch primitives values. I also like the fact that object instances can watch their own properties for changes rather than having a global static watcher. But then again, it’s up to you :).

3. Can I haz polyfill?

A few browsers (including IE) do not have support for watch or observe; sometimes you do not have the free choice of selecting a browser, so let’s move on to finding an implementation that can be used as a polyfill and work across browsers.

First, lets talk about getters and setters in JS; JavaScript has the concept of getters and setters which allow you to define functions that are invoked when you try to retrieve a value or set its value.

var obj = {};

Object.defineProperty(obj, "name", {
    get : function(){ return this._name;},
    set: function(val){  this._name = val;}
//undefined = "name";;

TIP: Can you figure out the bug in the following snippet?

var obj = {};

Object.defineProperty(obj, "name", {
    get : function(){ return;},
    set: function(val){ = val;}
//What happens?

Just as you thought, how about using this concept to fix this issue? Various browsers implement the getter/setter syntax differently, IE expects this format while Chrome expects this format. You could write a façade to hide this but again there is ugliness lurking around under the covers.

But great news! There is an excellent 5-year old polyfill by Eli Grey and it might meet your needs. Here is the gist and lets dive into its brilliance.

if (! {
   "watch", {
     enumerable: false,
     configurable: true,
     writable: false,
     value: function (prop, handler) {
       var old = this[prop];
       var cur = old;
       var getter = function () {
          return cur;
       var setter = function (val) {
        old = cur;
        cur =
        return cur;

       // can't watch constants
       if newval(delete this[prop]) {
            get: getter,
            set: setter,
            enumerable: true,
            configurable: true

How it works?

The function assumes knowledge of JavaScript’s accessor support (surprised that JS has getters and setters, yes there are subtle pockets of beauty in the language). The polyfill function accepts a property to watch and a handler to call when that function changes.

The handler function uses a beautiful trick – it redefines the watched value in the same scope, this ensures that the handler is always invoked when that value changes.

The trick to understanding the polyfill is in the second Object.defineProperty call. The value to be watched is first deleted to verify it is not a constant but this deletion will remove it from the scope. To counteract this, Eli’s brilliantly redefines the object in the same scope using Object.defineProperty. The getter is a plain getter while the setter is more involved – when the watched value is overridden or set to a new value, the setter is called. The closure in the watch object will invoke the handler after setting property to its new value. Neat huh?


Tip: A good way to debug is to use the debugger with an observed variable as shown below:

   function() { debugger; });

This will cause the debugger to kick in whenever the value changes and this helps a lot in tracking down bugs.

1. Eli’s polyfill didn’t fire in a deterministic manner when I tried it on Chrome but it did work.

2. Remember there are going to be performance implications but again this assumes you are in debugging mode :)

And that’s it! Feel free to copy over Eli’s implementation and use for your code.

SICP Review: Sections 3.1 & 3.2

Here are my thoughts on Sections 3.1 and 3.2 of my SICP journey; the deeper I go in the book, the more I appreciate the effort, style and work the authors put into it. Each section builds on earlier sections and it is amazing how it forces you to see software development from a new angle. Enjoy…

1. Perception and Design

Our perceptions of the real world influence the way we model objects in software development. The example in the book uses two objects – a rational number and a bank account. Modifications of a rational number always generate a new rational number that is different from the original (e.g. adding 1/2 to 3/8).  We do not ‘view’ rational numbers as being capable of changing – i.e. the value of 3/8 is always 3/8.

Conversely a bank account object has a balance that can ‘change'; withdrawals and deposit change this value even though we still say it’s the same bank account object! There appears to be a double standard right? If internal details (i.e. the numerator or denominator) of a rational number changes, we might end up with a new rational number however if a bank account balance changes we still have the same bank object.

Strange isn’t it, the way we think does influence the way we write code.

2. Environment

The environment (context) is very important in programming languages even though we just sometimes forget about it. Every language has some global environment that determines what expressions, values and parameters mean. If you change the environment, then it becomes highly possible that the expression has a different meaning.

Lets take an example from human languages, the word ‘wa’ means ‘come’ in Yoruba but can mean ‘and’ in Arabic. The same applies to programming languages, have you ever wondered why and where the + (a free variable) is defined? It has to be in the global environment so the programming language knows what it means. Now if you overrode the ‘+’ (why you’ll do this beats me) then it can mean something else.

3. Execution Order 

Exercise 3.08 was pretty fun, it was a simple ask – write a procedure such that evaluating (+ (f 0) (f 1)) will return 0 if the arguments to + are evaluated from left to right but will return 1 if the arguments are evaluated from right to left.

While the exercise is trivial and probably just an academic exercise, it shows how assignments can influence program execution. My solution uses a closure to track the number of calls and returns a unary lambda. The lambda returns the value of its argument on its first call and a zero on subsequent calls.

Thus if the execution order is left-to-right, then the first call will have a parameter of 0 and further calls will return 0 leading to (+ 0 0) while if it was right-to-left, then the first call will have a value of 1 and further calls will evaluate to 0.

JS and Scheme, the similarities

So I have been reading the awesome SICP book for some time now and the striking thing is the resemblance between JS and Scheme in some areas. Not surprising considering that Scheme was one of the major influences for the JS language. Thus, another reason to learn more languages to become a better programmer.

1. begin and comma operators

Scheme has the begin operator which allows you to evaluate a series of expressions and return the value of the last evaluated expression. This immediately struck me as being related to the JS comma operator which does exactly the same thing!

Code Samples


var x = 1,2,3,4,5;

x; //5


(define a

    (begin 1 2 3 4 5))

a; 5

One difference though is the fact that I find begin more useful in Scheme than its JS comma counterpart.

2. Type coercion

Due to JavaScript’s loose typing and implicit coercion, values can be truthy or falsy (e.g. undefined, null or 0). Idiomatic JavaScript leverages this coercion into false values and that’s why you see expressions like the below:

if(value) {

   console.log("Value is truthy!);


Scheme behaves in a similar way too – values are coerced albeit with more rigidity than JS.

(if null
   (display "Null coerced to true!")
; Null coerced to true!

3. Lambdas, first-class functions, closures and maybe lexical Scope

Some say JavaScript helped fuel the widespread adoption of lambdas in mainstream languages. It made people see the value of the hidden gem which hitherto had been languishing in the murky depths of academia.

Scheme and JavaScript do share first-class functions support, closures and lambdas. Although there is no lambda keyword in JS, anonymous functions essentially do the exact same thing. The introduction of let in ES6 should bring JS to par with Scheme’s let 

And that’s about it for now.

SICP Section 2.5

So four months after I started and more than 90 solved exercises, I can say Alhamdulillaah chapter 2 is done! Section 2.5 was one of the most challenging so far; the exercises revolved around building large easily extensible software systems. And here are the thoughts again :)

1. Coercion

The section revealed the importance of coercion in software development by creating a maths system for various numeric types (e.g. integers, rational, real and complex numbers). Since Scheme is not an OOP language, procedures were used to encapsulate each numeric type’s functionality and ‘install’ them into the system. A generic apply function multiplexes routing calls to the matching number type interface.

The challenge was in handling mixed operations (e.g. adding an integer to a complex number or vice versa). Since numeric types are progressively subsets of one another (i.e. an integer is also a complex number but not vice versa), it is possible to build the following hierarchy of types.

integer -> rational -> real ->complex

By leveraging this hierarchy, it becomes possible to do arithmetic operations on a mixed variety of number types. The system just needs to ‘raise’ all the numbers to topmost type in the hierarchy and then invoke the arithmetic operation.

Lets take it further one bit; if ‘raise’ is possible, then surely ‘demote’ can be done too! A good application of this is ensuring that the results of ‘raised’ operations are in their simplest terms. For example, the result of adding these mixed numbers types: 1, 1 – i, 2.0, -1 + i is 3 + 0i. The result can be progressively reduced to the integer 3 as shown below:

3 + 0i -> 3.0 -> 3 / 1 -> 3

And the icing on the cake? There is no need to write a demote function! The earlier raise procedure should demote number types if its hierarchy of types is reversed.

These assumes the system already supports two-way converters for each boundary of the hierarchy (e.g. integer->rational and rational->integer). There is no need for an integer->real converter since integers can be promoted to rational numbers and then to real numbers.

Neat huh?

2. Extending a system

Assume the system above has to support polynomial arithmetic. It turns out it is quite simple to do – why not view polynomials as another layer in the hierarchy of types? This new hierarchy is shown below:

integer -> rational -> real ->complex->polynomial

An interesting abstraction isn’t it? It works too once all the interfaces are implemented. Designing software is indeed art…

Some extra learning points were the representation of polynomials: the sparse and dense approaches both had their pros and cons. A better fix was to enable the system to support both representation formats (simple trick: convert one form to the other and everything should be all good :) ).

Another interesting trick was polynomial subtraction; there is really no need to implement subtract if you already have addition implemented. All you have to do is add ‘negated’ versions of each polynomial term. Simple but powerful.

On to chapter 3 insha Allaah!

How to write a Promise/A+ compatible library

I decided to write Adehun after the series of promise posts. Adehun means ‘Promise’ in Yoruba, the language of my West African tribe. After a lot of rewrites, I can say Alhamdulillaah,  Adehun passes all the compliance tests of the promise/tests repo.

And here is how I did it.

1. Promise States & Validity

An object mapping the various states to integers and an isValidStates function to verify that only valid states are accepted.

2. Utils

A couple of helper functions including runAsync (to run functions asynchronously), isObject, isFunction and isPromise helper functions.

3. The Transition function – Gatekeeper for State Transition 

Gatekeeper function; ensures that state transitions occur when all required conditions are met.

If conditions are met, this function updates the promise’s state and value. It then triggers the process function for further processing.

The process function carries out the right action based on the transition (e.g. pending to fulfilled) and is explained later.

function transition (state, value) {
  if (this.state === state ||
    this.state !== validStates.PENDING ||
    !isValidState(state)) {

  this.value = value;
  this.state = state;

4. The Then function

The then function takes in two optional arguments (onFulfill and onReject handlers) and must return a new promise. Two major requirements:

1. The base promise (the one on which then is called) needs to create a new promise using the passed in handlers; the base also stores an internal reference to this created promise so it can be invoked once the base promise is fulfilled/rejected.

2. If the base promise  is settled (i.e. fulfilled or rejected), then the appropriate handler should be called immediately. Adehun.js handles this scenario by calling process in the then function.

function then (onFulfilled, onRejected) {
 var queuedPromise = new Adehun();
 if (Utils.isFunction(onFulfilled)) {
   queuedPromise.handlers.fulfill =

 if (Utils.isFunction(onRejected)) {
   queuedPromise.handlers.reject =


 return queuedPromise;

5. The Process function – Processing Transitions

This is called after state transitions or when the then function is invoked. Thus it needs to check for pending promises since it might have been invoked from the then function.

Process runs the Promise Resolution procedure on all internally stored promises (i.e. those that were attached to the base promise through the then function) and enforces the following Promise/A+ requirements:

1. Invoking the handlers asynchronously using the Utils.runAsync helper (a thin wrapper around setTimeout (setImmediate will also work)).

2. Creating fallback handlers for the onSuccess and onReject handlers if they are missing.

3. Selecting the correct handler function based on the promise state e.g. fulfilled or rejected.

4. Applying the handler to the base promise’s value. The value of this operation is passed to the Resolve function to complete the promise processing cycle.

5. If an error occurs, then the attached promise is immediately rejected.

function process () {
 var that = this,
     fulfillFallBack = function (value) {
       return value;
     rejectFallBack = function (reason) {
       throw reason;

 if (this.state === validStates.PENDING) {

 Utils.runAsync(function () {
   while (that.queue.length) {
     var queuedP = that.queue.shift(),
     handler = null,

       handler = queuedP.handlers.fulfill ||
       handler = queuedP.handlers.reject ||

     try {
       value = handler(that.value);
     } catch (e) {

   Resolve(queuedP, value);

6. The Resolve function – Resolving Promises

This is probably the most important part of the promise implementation since it handles promise resolution. It accepts two parameters – the promise and its resolution value.

While there are lots of checks for various possible resolution values; the interesting resolution scenarios are two – those involving a promise being passed in and a thenable  (an object with a then value).

1. Passing in a Promise value

If the resolution value is another promise, then the promise must adopt this resolution value’s state. Since this resolution value can be pending or settled, the easiest way to do this is to attach a new then handler to the resolution value and handle the original promise therein. Whenever it settles, then the original promise will be resolved or rejected.

2. Passing in a thenable value

The catch here is that the thenable value’s then function must be invoked  only once (a good use for the once wrapper from functional programming). Likewise, if the retrieval of the then function throws an Exception, the promise is to be rejected immediately.

Like before, the then function is invoked with functions that ultimately resolve or reject the promise but the difference here is the called flag which is set on the first call and turns subsequent calls are no ops.

function Resolve(promise, x) {
  if (promise === x) {
    var msg = "Promise can't be value";
    promise.reject(new TypeError(msg));
  else if (Utils.isPromise(x)) {
    if (x.state === validStates.PENDING){
      x.then(function (val) {
        Resolve(promise, val);
      }, function (reason) {
    } else {
      promise.transition(x.state, x.value);
  else if (Utils.isObject(x) ||
           Utils.isFunction(x)) {
    var called = false,

    try {
      thenHandler = x.then;

      if (Utils.isFunction(thenHandler)){,
          function (y) {
            if (!called) {
              Resolve(promise, y);
              called = true;
          }, function (r) {
            if (!called) {
              called = true;
     } else {
       called = true;
   } catch (e) {
     if (!called) {
       called = true;
 else {

7.  The Promise Constructor

And this is the one that puts it all together. The fulfill and reject functions are syntactic sugar that pass no-op functions to resolve and reject.

var Adehun = function (fn) {
 var that = this;

 this.value = null;
 this.state = validStates.PENDING;
 this.queue = [];
 this.handlers = {
   fulfill : null,
   reject : null

 if (fn) {
   fn(function (value) {
     Resolve(that, value);
   }, function (reason) {

I hope this helped shed more light into the way promises work.

AdehunJS on Github.

Liked this post? Here are a couple more exciting posts!

1. The Differences between jQuery Deferreds and the Promises/A+ spec

2. Programming Language Type Systems II

3. Three Important JavaScript Concepts

SICP Sections 2.3 & 2.4: Thoughts and Ideas

1. Top-Down Design

Most of the problems in the SICP book are solved in a top-down way with lower level details deferred until needed. The focus on high level details makes for expressive flexible code since implementation is based on well-defined interfaces and not implementations. Consequently, swapping and improving interfaces is a cinch – the dependency on high level interfaces shields consumers from tight coupling to details.

Picture a calculus system for integer operations (e.g. differentiation); as expected, these higher order functions need support for addition/subtraction/division operators. A top-down design will implicitly assume the add/subtract operators will work appropriately and focus on the higher level operators first. These low-level details can even be mocked and the system unit tested to prove logic accuracy.

The benefit of such a delayed implementation comes when the system needs to be extended to support complex numbers – the higher level operations are still the same, only the low level add/subtract operators need to be changed. Once done, the entire system should work as before.

Design is subjective and this is one approach; there might be better ways for other scenarios but again this can be tried and leveraged.

2. Generics: Data-directed vs Message-Passing Styles

Generics are useful for maintaining and extending existing systems.

The data-directed style maintains a huge table of known procedures and looks up desired operations based on keys. Extending such systems involves adding the new function signatures to the table; this allows the entire system to start using the newly added functions (sounds like the adapter pattern).

The downsides include the extra overhead of a procedures’ table, the need for consumers to know about available operations and the enforcement of contracts in the system. However, benefits such as flexibility, encapsulation and ease of accretion of new features can’t be ignored.

Message passing involves sending messages to objects to trigger the right operations. Sounds familiar? Yes, it does sound like OOP and is one of its major tenets. An object keeps a list of possible operations and invokes the right method based on the message it receives (think of invoking an instance method in an OOP language as sending a ‘message’ to the object e.g. a.b() ).

Languages like Python and JavaScript allow for a clearer example of the message passing metaphor since they allow creating hashtables of functions. With such a hashtable, you can invoke a function by sending a ‘message’ to such functions e.g. a[“b”]().

The question thus comes to mind, how do programming languages handle their primitive operations at a lower level? Do they do message passing? Keep tables?

3. Context

The importance of context and how they affect semantics in all languages (both human and programming).

Using the example from the book; three is equal to one plus two however ‘three’ is not equal to “one plus two”. The context determines what is meant and humans have long used this to create various literary forms such as puns, sarcasm etc.

The same applies to programming, context does determine if your program will run perfectly or if it will have subtle but difficult to find errors. One obvious example is the JavaScript that = this pattern.

4. Huffman Encoding

Some computer science and maths eh, the Huffman encoding scheme allows you to use much less space provided the conditions are met.

5. Understanding the problem deeply before writing any code

Coding should be simple. Unfortunately, a shallow understanding the problem being solved makes it a difficult task.

Spending a good chunk of time to really understand the programming task at hand has great benefits, it leads to well-thought out designs, simple models (simple is difficult!) and less bugs. It also reduces the chances of providing the wrong solution. It makes for a more fulfilling experience too – what beats the thrill of having zero compile errors on the first attempt?

If you liked this post then do check out a couple more exciting discoveries from my SICP journey:

1. SICP Section 2.2: New ideas and thoughts about programming
2. SICP Section 2.1 : Thoughts
3. SICP Section 1.3 : Thoughts and Concepts

Manipulating lists with JavaScript Array methods

Lists of all sorts (e.g. grocery, to-do, grades etc) are a normal facet of our daily lives. Their ubiquitousness extends to software engineering as many programming tasks require manipulating lists. This post exposes new ways of doing popular list manipulation tasks in JavaScript and a little more too.

Be warned! JavaScript array methods routinely expose amazing surprises. Other languages generally tend not to have such hidden surprises, but well, don’t you just love the extra excitement from JavaScript? Afterall, it’s the language we all love to hate ;).

1. Appending arrays

One of the most common tasks is appending an array to the end of another array; Array.prototype.concat works but forces you to create a temporary variable or overwrite one of the arrays.

A clever use of Array.prototype.push allows getting by both limitations.

var a = [1,2,3];
var b = [4,5,6];
//requires temp value or overwriting
a = a.concat(b);

//using push
a.push.apply(a, b);
//similar variations

How does this work? The Array.prototype.push method will append its arguments to the end of the array it’s called on.


The Function.prototype.apply method applies a method to an object and overrides arguments (an array-like object containing the arguments passed to a function). Apply allows you to invoke a function on any object with whatever parameters you want.

The problem is thus solved by applying push to and providing b as the arguments to be pushed. The code snippet is the same as shown below:

a.push(b1, b2, ..., bn);

This trick can be generalized and used anywhere once the second array can be transformed into an arguments object. The Math.max and Math.min methods, which return the max/min value of a set, provide another example.

Using the trick above, an array’s max value can be found by applying the Math.max method on the array.

var a = [1,2,3,4,5];
var max = Math.max.apply(Math, a);


Note that the object has to be Math here since it contains the max function. Isn’t this neater than iterating over the array to find the max value?

Tip: Apply differs from Call which expects an array; if Call were to be used, then gets inserted as a single element. Can you figure out how to use Call to achieve the same results?

2. Pushing into objects

Do you know Array.prototype.push works on non-array objects too? Huh? Didn’t I warn about JS Array methods earlier on?

Push is a generic function and provided the object it is applied to meets its requirements it’ll work; otherwise, tell me when you find out.

An example is shown below (anyone that does this in real-life code should be barred from touching code for weeks…).

var obj = {};

[].push.apply(obj, [1]);
Array.prototype.push.apply(obj, [2,3,4]);
//{0: 1, 1: 2, 2: 3, 3: 4, length: 4}

How did the length property get there?! The ES5 push spec explains this: the 6th step of the push operation will put length and the number of elements as a key-value pair into the object push was called on. Do you now know why it is [].length and not [].length()?

The tale continues, how about splicing some lists?

3. Merging arrays and removing elements at specified locations

The Array.prototype.splice method is probably mostly used for removing elements from JS arrays. Why it’s called splice and not remove is baffling but since it allows you to simultaneously insert elements I guess that name works. You can splice rope strands huh…

You can actually ‘delete’ array entries using the delete operator, however delete will leave behind undefined at the deleted element’s index; typically not what you want in most scenarios.

SideTip: The delete operator accepts expressions as operands and will evaluate such expression operands! E.g. delete (b=4) works!!

var a = [1,2,3];
delete a[1];
//[1, undefined, 3]

Fix? Lets splice!

var a = [1,2,3];
var removed = a.splice(1,1);

The splice operation can also be used to insert an array’s elements into another array at a certain index, how? You probably guessed that now – it is the apply trick again.

var a =[1,2,6];
var b = [3,4,5]

a.splice.apply(a, [].concat(2, 0, b));

Confused about the need for [].concat? Apply expects an array of parameters that match the function’s signature; these are passed through as the arguments. The splice call to merge the arrays has the  2, the position to insert at; 0, the number of elements to remove and b, the array of elements to be merged.

The concat call translates into [2,0,4,5,6] and this is equivalent to the call below:


4. Truncating and Emptying arrays

var a = [1,2,3];
a.length = 0;


a.length = 2;

//Or filling up arrays with undefined
a.length = 3;

The ES5 spec explain this, once length is set to a value less than the original length, all outlying elements will be deleted.

So why did I write the post?

1. So you know what to do if you ever run into these ‘smart’ pieces of code

2. To expose some of the other side of JS.

Hope you liked it, here are some more fun posts ;)

1. Three Important JavaScript Concepts
2. Understanding Tail Recursion
3. Understanding Partial Application and Currying

7 Cool tricks with Chrome DevTools

1. $_

$_ re-evaluates the last expression and is similar to the ‘_’ command in python’s REPL. However _ prints the last ‘non-None’ value while $_ prints the value of the last evaluated expression even if it is undefined.


2. $() and $$() selectors

$() selects the first matching DOM element while $$() selects all matching DOM elements. Quite useful if jQuery is missing.

$$ selectors

Tip: $() and $$() are aliases for document.querySelector() and document.querySelectorAll() respectively; if any of them gets overridden, simply re-assign the function references. You can even create your own shortcuts!

$ = document.querySelector;
slct = document.querySelector;

3. Console.table

Very good for generating a nice view of an object in the console. Saves you from having to click about. It works with non-uniform object types too.


4. DOM Element event listeners

There are two ways to do this:

1. Select an element in the elements view and then go to the event listeners tab in the elements view.

2. Select the element in the console and call getEventListeners on the object


5. $0 – $4

This  refer to the first through fifth last selected items. These shortcuts are handy references to pre-selected DOM elements.


6. Drag Drop in Elements View

It is possible to re-order the DOM layout structure using drag drop gestures in the elements view. No need to get your hands dirty anymore – just drag/drop.

7. copy

Useful for directly copying JavaScript objects to the clipboard. Call copy on the object (as shown below) and paste wherever you want it.


And that’s it! Hope you learnt one or two new tricks to enhance your development.

SICP Section 2.2: New ideas and thoughts about programming

Here are 5 points to think about, hopefully they’ll trigger an ‘aha’ moment.

1. Leveraging the ‘Closure’ concept in programming

I am not talking about the ‘closure’ concept from programming (the one that involves free variables). This refers to the ‘closure‘ concept from mathematics and the power it brings to programming languages.

Mathematically, a set is closed under an operation if carrying out that operation on elements of the set will always give a result that is also in the set. For example, addition of positive integers is closed under the set of positive integers (subtraction is not: 1 – 3 = -2; which is not positive). Thus you can infinitely add positive numbers without worrying about ending up with non-positive numbers.

Lets take this to programming: closed operations allow chaining because the result will be always be a valid value for further operations. Does this strike a bell? Think fluent programming, think jQuery chains. This simple concept allows very complex actions by leveraging simple data and procedures.

2. Simple it might seem, difficult it might be

I initially thought Ex 2.18 would be dead easy; it was simple: reverse a scheme list. I realized my folly after spending the better part of an hour battling the task. Unlike pointer-based lists in C-like languages, the Scheme list is a chain of pairs, each pair points to another pair and so on. This negated walking down the list and reversing pointer directions.

Recursion was tricky since getting the second half of a list pair (i.e. cdr list) brought the entire sublist instead of just the element! The reversal task involved the following restrictions:

  • No variables allowed
  • Only the top element of a sublist can be retrieved while walking down the list
  • The procedure should recursively construct a new list simultaneously
  • Retrieval of elements at arbitrary positions is difficult; although possible, the absence of variables makes this useless.

My first approach worked but created a wrong data structure; Alhamdulillaah I eventually got the right solution. An elegant solution I found online solved the reversal problem simply – reversing a list is equivalent to appending the first element of the list to the reversal of the remaining elements.

3. Languages influence thinking and problem modelling

Languages play a big role in the way we think and write code, most of us see programming tasks through an ‘imperative’ pair of glasses. Alhamdulillah the SICP book is giving me a new pair of glasses. Don’t we all need a new pair of glasses every now and then?

Imagine a simple task: calculate the sum of the squares of the odd numbers in an array. The snippets below show both the imperative and functional styles of solving the problem.

function isOdd(num) {
    return (num %2 ) === 1;

function square(num) {
    return num * num;

//imperative style
function sumOddSq(arr) {
  var sumOddSq = 0;
  for(var i=0, l=arr.length; i < l; i++) {
      if(isOdd(arr[i])) {
          sumOddSq += square(arr[i]);
  return sumOddSq;

//functional style
function sumOddSq2(arr) {
  return arr.filter(isOdd)
            .reduce(function (val, acc) {
                return val + acc;
            }, 0);

sumOddSq([1,2,3]);   //10
sumOddSq2([1,2,3]);  //10

The imperative approach involves walking through the array, checking for odd numbers, squaring those odd numbers and updating the sum as you go along. Using a functional programming approach, the task involves filtering the sequence for odd numbers, mapping the filtered numbers to their squares and then reducing these squares to a sum.

Functional programming relies heavily on the map, reduce and filter concepts and most problems can be solved based on these building blocks. For tasks involving non-list-like structures, converters can be used to create sequences and also generate structures from list results.

Did you notice? sumOddSq2 chains array operations; the map, reduce and filter operations can be said to be ‘closed’ over the set of arrays. Thus the result of any of these operations is always an array and can be reused immediately.

4. Software engineering is still engineering

Even though software engineers manipulate bits and not ‘real’ hardware; general engineering concepts still apply. There are a couple of interesting similarities; for example in electronics engineering, the diode can be viewed as an if statement while filters can be viewed as ‘filter‘ logic. The electronics engineer uses a filter, the software engineer uses a filter function. Both combine them to build even more complex abstractions.

Another example is the computer system and the various dependent layers; a simplified complexity order is shown below:

Transistors -> Logic gates  -> Circuits -> Processors -> Computers -> Distributed Systems

The same applies to software engineering, an application can be seen as:

Values -> Methods -> Classes -> Models -> Subsystems -> Modules -> Applications

We all start from something simple and progressively add complexity, each layer being shielded from the layer below it. A computer engineer can swap processors without breaking a computer; so should a software engineer be able to swap out layers in his code without bringing down the application.

5. Recursion

The 8 queens problem is one that a lot of people are familiar with and as you guessed, there was an exercise on that. The solution was quite simple – recursively build the board starting out with one queen. As the board grows, filter out positions that have queens attacking each other, finally return the list of all positions that have safe queens.

The functional-programming styled solution involved enumerating all positions, creating a safe? function to determine a valid board, passing this function to the filter and then outputting the results. It is great to see how functions can be composed and how the basic functional programming support for map, reduce and filter enable even higher levels of complexity.

I must admit I struggled a lot with this problem as it was difficult to understand and debug – walking up the call stack did expose some of its intricacies but it’s amazing how simple and how yet powerful the results of combining these simple concepts.


And that’s about it. Next is section 2.3 with about 20 questions; I skipped about 2 or 3 questions in 2.2 since I didn’t really feel they were worth solving. I hope I can get 2.3 done by mid January insha Allaah and create another post then!

Related Posts

Here are a couple more of my SICP-themed posts

1. SICP Section 2.1 : Thoughts

2. 5 things I have gained from SICP: Section 1.2

3. SICP Section 1.3 : Thoughts and Concepts