10 Programming Principles That Make You Better at Coding Interviews
You have probably seen these programming principles on a poster, a blog post, or a slide deck. KISS. DRY. YAGNI. They sound like good advice for writing production code. But they are not just production advice. Every one of these principles makes you measurably better at solving coding interview problems. Here is how.
Simplicity & Readability
KISS
Keep it simple. Avoid unnecessary complexity.
DRY
Don't repeat yourself. Extract shared logic.
Clean Code
Readable code beats clever code.
Structure & Organization
Single Responsibility
Each function does one thing.
Separation of Concerns
Independent parts, clean boundaries.
Composition
Build from small, reusable pieces.
Process & Discipline
YAGNI
Only build what you need right now.
Open/Closed
Extend without rewriting.
Refactor
Improve code after it works.
Document
Explain intent so others can follow.
The interview connection
Most candidates treat coding interviews as a completely separate skill from writing real software. But the habits that make you a good engineer are the same habits that make you a good interviewee. These ten principles map directly to concrete interview behaviors.
Keep It Simple = Don't over-engineer. Brute force can be correct.
See: Valid Palindrome
Don't Repeat Yourself = Extract helpers for repeated pointer/logic work.
See: LRU Cache
Single Responsibility = Each helper function does exactly one thing.
Separation of Concerns = Separate parsing from processing.
Refactor = Start brute force, then optimize.
See: Two Sum
Clean Code = Readable variable names, consistent structure.
See: Maximum Subarray
Let's walk through each one.
1. KISS (Keep It Simple, Stupid)
Write code as simply as possible. Clear variable names, no unnecessary complexity, no over-engineering.
In interviews, this means: do not reach for a complex solution when a simple one works. The brute force is sometimes the right answer. In Valid Palindrome, two pointers from opposite ends is the simplest approach. You do not need to reverse the string, use regex, or allocate extra arrays. Two pointers, one loop, done. Interviewers reward candidates who pick the simplest correct approach and implement it cleanly.
2. DRY (Don't Repeat Yourself)
Avoid duplication of data or logic. Use loops, helpers, and abstractions instead of copying code.
In LRU Cache, the clean solution extracts _add_node and _remove_node as helper methods. Without them, you end up copy-pasting pointer manipulation into both get() and put(). That duplicated code is a bug factory. One wrong pointer update in one copy and you spend 15 minutes debugging. DRY is not just good practice. It is a survival strategy under time pressure.
3. Open/Closed Principle
Code should be open to extension but closed to modification. You should be able to handle new inputs without rewriting core logic.
In Coin Change, the DP recurrence dp[amount] = min(dp[amount], dp[amount - coin] + 1) handles any set of denominations. You never need to change the algorithm when the coin set changes. This is the Open/Closed principle at work: the solution extends to new inputs without modifying the core logic. Build your interview solutions so they handle edge cases naturally, not through a pile of if-statements bolted on at the end.
4. Composition Over Inheritance
Objects with complex behaviors should contain instances of simpler objects rather than inheriting from a deep class hierarchy. Prefer building from small, reusable pieces.
In Merge K Sorted Lists, the clean approach composes two building blocks: a min-heap to track the smallest current node across all lists, and a linked-list merge to stitch the result together. Neither piece is complicated on its own. The power comes from combining them. This is how the best interview solutions work. You do not write one monolithic algorithm. You snap together small, well-understood building blocks.
5. Single Responsibility
Every class or function should do one thing and do it well.
In Serialize and Deserialize Binary Tree, serialize() converts a tree to a string. deserialize() converts a string back to a tree. Each function has one job. If you tried to combine both directions into a single function, the logic would be tangled and hard to debug. Keeping responsibilities separate means each piece is easy to write, easy to test, and easy to explain to your interviewer.
6. Separation of Concerns
Design your solution with distinct, independent parts that handle different aspects of the problem.
In Evaluate Reverse Polish Notation, the stack management is separate from the arithmetic operations. The stack handles ordering and operand tracking. The arithmetic section handles computation. Mixing these two concerns together creates messy, hard-to-follow code. Keeping them apart makes each piece simple and the overall solution easy to walk through during an interview.
7. YAGNI (You Aren't Going to Need It)
Do not write code for hypothetical future needs. Only implement what is required right now.
In interviews, this means: solve the problem as stated. Do not build a generic framework when a specific solution is what is needed. Do not add unnecessary edge case handling for inputs the problem guarantees will not exist. Do not prematurely optimize a solution that already meets the time complexity requirement. Every line of code you write that is not needed is a line that could contain a bug and a line that wastes your limited time.
8. Document Your Code
Write clear comments and explanations so others can understand your intent.
In interviews, "documentation" happens in real time. You talk through your code out loud as you write it. "This loop finds the first mismatch." "This variable tracks the running maximum." "I am using a hash map here because I need O(1) lookups." This running commentary is the interview equivalent of good documentation. It shows the interviewer you understand what you are building, and it gives them a chance to redirect you if you are heading off track.
9. Refactor
Review and optimize code regularly. Make it more efficient while keeping the results the same.
This is the natural flow of a good interview. Start with Two Sum: the brute force is O(n^2) with nested loops. It works. It is correct. Now refactor. Replace the inner loop with a hash map lookup. Same result, O(n) time. The interviewer sees that you can get a working solution first and then improve it. That is exactly how professional engineers work, and it is exactly what the Refactor principle is about.
10. Clean Code at All Costs
Prioritize readability over cleverness. If someone else cannot read your code, it does not matter how clever it is.
In Maximum Subarray, naming your variables current_sum and best_sum makes the algorithm instantly clear. Naming them a and b forces the reader to reverse-engineer your intent. In an interview, the reader is your interviewer, and they are evaluating you in real time. Clean variable names, consistent indentation, and logical structure are not optional. They are part of your score.
The takeaway
These principles are not abstract wisdom. They are concrete habits that save you time, reduce bugs, and make your code readable to interviewers. The best part: they stack. Writing simple, non-repetitive, single-responsibility code with clear names is not ten separate skills. It is one skill, and it gets better with deliberate practice.
You do not need to memorize these as a checklist. Practice enough problems with good habits and these principles become automatic. That is the goal.
Related posts
This is part of a series connecting software engineering concepts to coding interview skills:
- Implementation in Software Engineering and Coding Interviews covers technical debt, coding principles, and buy vs. build.
- Software Architecture Patterns in Coding Problems maps pipe-and-filter, client-server, master-slave, and layered patterns to algorithm design.
- How the SDLC Applies to Solving Coding Problems maps the five SDLC phases to problem-solving steps.