How to Solve Coding Problems with a Simple Four Step Method
As a senior engineer, I‘ve designed complex systems for Fortune 500 companies, contributing critical code that powers million-dollar campaigns. But early in my career, I used to panic when faced with even simple coding challenges.
My turning point came during a disastrous whiteboard interview. I choked on FizzBuzz of all things – a common screening dev quiz that I could solve today with my eyes closed.
That job rejection stung, but it forced me to completely change how I approached coding problems through adopting an invaluable yet underrated framework called Poly‘s Four Step Method. Originating from mathematician George Poly‘s seminal 1945 book "How To Solve It", it provides a tried and true blueprint for breaking down problems through a simple 4 phase approach:
- Understand
- Plan
- Solve
- Look Back
Now when tackling hairy bugs or tricky interview questions, I confidently reach for this reliable system to methodically plot a path to resolution. And after coaching over 100 students through coding bootcamps, I‘ve witnessed countless "aha moments" as they master this technique.
In this comprehensive guide, we‘ll walk through real coding examples to demonstrate Poly‘s approach in action within the context of programming interviews and debugging scenarios. Master this repeatable 4 step method, and you‘ll be able to analyze any coding riddle or puzzle placed before you with clarity and precision.
Step 1: Building a Crystal Clear Understanding
Imagine you‘re crafting an intricate, multi-tier cake. Now imagine jumping right to piping on decorative rosettes before properly baking the layers to hold everything together. Messy right?
Yet that‘s exactly what coders do when they dive in before understanding a problem‘s inputs, outputs and requirements inside out. Skipping this foundation leads to cracked solutions that ultimately collapse under scrutiny.
I learned this the hard way back in my interview horror story days. I‘d constantly make assumptions about problem specifics or constraints leading me down wayward paths. Now I take nothing for granted, slowly building an unshakeable comprehension.
Let‘s break down key tactics I follow in Phase 1:
Read the Prompt…5 Times Over
When presented a coding prompt, enthusiastically dive in…by carefully reading and re-reading up to 5 times before writing any code.
Yes, it may feel tedious, especially with the clock ticking during whiteboarding interviews. But glossing over details almost always comes back to bite you. I vividly remember spending 20 minutes coding a solution just to finally notice the prompt specified returning the second largest number…not the first. Ugh!
On each pass of the prompt, hunt for these key details:
- Function name
- Required parameters
- Return value
- Error handling needs
- Constraints
Circle or highlight anything that‘s unclear after multiple reads. Ask clarifying questions if possible rather than making assumptions.
You might be wondering "Won‘t I waste time upfront doing this?" Surprisingly no – a research study found developers who spend more time understanding ambiguities early on take less time coding overall solutions.
So slow down…your solution will speed up!
Map Out Example I/Os
Along with thoroughly parsing requirements, I write out 3-5 input/output examples covering simple to complex scenarios.
For example, if asked in an interview:
Write a function that merges two sorted arrays into one sorted array
I‘d jot down:
Input 1: [1, 5, 7], [2, 6] Output 1: [1, 2, 5, 6, 7]Input 2: [], [1, 2] Output 2: [1, 2]
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Starting basic exposes gaps in understanding. Are duplicate values allowed? Should empty inputs return null or empty array? Covering edge cases prompts critical thinking.
These examples essentially become sanity checking test data as I progress through the steps. I cannot stress enough how much time and struggle this practice has saved me during marathon debugging sessions!
Ask Clarifying Questions
Don‘t hesitate to speak up on ambiguities, whether during interviews or requirements planning meetings at work.
Common areas I poke and prod on:
- Data types/structures
- Allowed libraries
- Edge case handling
- Performance constraints
- Test data
Early back-and-forths prevent wasted effort from misguided development. Recently while pair programming, my partner proposed using a set for storage believing it handled duplicates. I politely asked if his assumption was correct before charging ahead. Turns out we explicitly wanted to retain duplicates…pivoting to an array avoided a costly gut & replace later on.
Think of questions not as admitting ignorance but demonstrating rigor and maturity through exhaustively eliminating uncertainties.
Document Decisions
I always keep a running doc of notes, assumptions, questions and prompts during Phase 1. If whiteboarding, I‘ll scribble on scratch paper. Within IDEs, I‘ll comment decisions directly atop modules.
// Assumptions: // 1) Inputs always arrays // 2) Outputs can include duplicate values // TODO: Handle invalid input types
This documentation becomes invaluable if I need to revisit solved problems months later for enhancements, audits or even interviews!
By the end of Step 1, I feel equipped to teach the problem to someone else. That level of intimate understanding sets me up to smoothly plan out implementations next.
Step 2: Mapping the Terrain with Pseudocode
Understanding solved, I shift to architecting approach through pseudocode. Interchanging "real code" for "pseudo code" enables quickly diagramming logical flow at a high level before sweating syntax details.
Take this prompt:
Given an array of points on a 2D plane, write a function that returns the 2 points with the shortest distance between them
Before diving into classes or frameworks, I map out my conceptual solution:
Function findShortestDistance
Loop through all points
Nested loop to pair with every other point
Calculate distance between points
If distance < currentShortest
Store points as currentShortest
Return currentShortest points
Pseudocode untethers me from language specifics to prototype the "shape" of the solution‘s logic. I can tweak, enhance, or even pivot approaches rapidly without wasting effort on throwaway code.
And verbally explaining pseudocode out loud surfaces holes in logic way before expensive debugging. I‘d catch a redundancy in the above nested loops for example, preventing wasted effort.
Interestingly, researchers at UC Irvine discovered programmers with more scribbles and pseudocode sketches in work journals were able to handle complex problems better than those who wrote less. So don‘t be shy…sketch away with pseudocode!
Top Tips for Crafting Pseudocode
With practice, you‘ll develop shorthand syntax that communicates context quickly. But when just getting started, keep these tips in mind:
Avoid Language Specifics
Terms like Loop, Print, Function convey purpose without coding semantics. Steer clear of If/Else, Declare, Define that map to particular languages.
Use Plain Language
Favor literal over complex terms. "Find shortest distance" not "Execute proximity evaluation".
Visualize Logic Flow
Use indentation, newlines and markers (Then, Next, Finally) to diagram control flow.
Verbalize It
Explain logic out loud. If terms feel awkward or sequencing confusing, refine.
Following an ambiguous map still leaves you lost, even if directions are perfectly coded. So be meticulous and precise in Phase 2 to set up smooth sailing ahead.
Step 3: Code Safely Guided by Pseudocode Compass
With crystal clear specs, thoughtful pseudocode, and test cases in hand, I feel supremely confident beginning coding implementation.
I translate pieces of pseudocode into real code line-by-line like a roadmap guiding me safely along. I don‘t divert or take shortcuts. Doing so either leaves me lost requiring backup maneuvers or worse – introduces bugs through misalignment.
Let‘s implement our 2D point distance checker from earlier:
function findShortestDistance(points) {
let currentShortest = Infinity;
// Track closest pair
let closestPair;
for (let i = 0; i < points.length; i++) {
let point1 = points[i];
for(let j = i + 1; j < points.length; j++){
let point2 = points[j];
// Calculate distance
let distance =
Math.sqrt(
(point2[0] - point1[0]) ** 2 +
(point2[1] - point1[1]) ** 2
);
// Update shortest
if(distance < currentShortest){
currentShortest = distance;
closestPair = [point1, point2];
}
}
}
return closestPair;
}
See how my pseudocode provided perfect step-by-step guidance to churn out a clean initial solution? No dead ends or unfinished branches thanks to the blueprint.
At this phase, I emphasize correctness over efficiency, clarity over conciseness. The core logic needs to align with specifications before considering optimizations. Think of chiseling a sculpture from clay – get the form right before polishing!
With a working solution coded up per blueprint specs, I can confidently move onto final refinements.
Step 4: Finding Diamonds Through Refinement Pressure
So I‘ve reached an initial solution by sticking loyally to understanding, planning and coding phases. Time to celebrate getting this far right?
Not so fast! This final "look back" step takes solutions from coal to diamonds through applying refinement pressure from all angles.
I set ego aside and critically challenge my code from every viewpoint:
- Are variables/functions named intuitively?
- Can logic be simplified?
- How might inputs/outputs expand in the future?
- What edge cases could still break things?
- Does performance meet constraints?
- Can code be understood easily at a glance?
I walk through questions verbally or pair program for fresh perspectives. Recently I revamped an algorithm to assign delivery routes more efficiently. My initial solution worked but barely beat random assignment. Talking through places to optimize with a colleague revealed far superior clustering techniques.
Sometimes I learn alt solutions that spark inspiration. After barely solving a whiteboard prompt, I looked up examples online finding recursive patterns way cleaner than my iterative approach.
Think of this phase like honing a standup comedy routine. Joke punchlines often come out awkward at first. But through gathering crowd feedback and self-analysis, the phrasing refines into perfect, concise hilarity generating laughter every time.
Master Problem Solving Through Consistent Practice
After years applying Poly‘s framework across countless coding challenges, I firmly believe anyone can strengthen problem-solving skills through consistent practice.
Internalize these 4 phases to tackle any puzzle systematically:
1. Understand – Clarify objectives, requirements and test cases
2. Plan – Map logic flow with pseudocode
3. Solve – Code solution adhering to blueprint
4. Reflect – Refine code for improvements
At first, work through toy problems or old assignments applying the steps deliberately. Over time, the process becomes second nature. You‘ll kickstart understanding, plan out logic jumps naturally, code with precision guided by blueprint, and review optimiziations instinctively.
Strive to broaden coding exposure to build pattern recognition. Catalog types of problems faced to review solutions later. Battle test new languages and frameworks with simple challenges.
Doing so accumulates a deep, tacit pattern library so you react "I know I‘ve solved this puzzle before" instead of panicking.
So stay curious, hungry for knowledge, while leaning on this reliable framework when tackling novel problems. You‘ll be shocked how effectively you start systematically developing solutions, escaping even the most convoluted coding riddles thrown your way!
Now go solve something with confidence!
