code-quality-principles

Code Quality Principles

Guidance on KISS, YAGNI, and SOLID principles with language-specific examples.

When To Use

• Improving code readability and maintainability
• Applying SOLID, KISS, YAGNI principles during refactoring

When NOT To Use

• Throwaway scripts or one-time data migrations
• Performance-critical code where readability trades are justified

KISS (Keep It Simple, Stupid)

Principle: Avoid unnecessary complexity. Prefer obvious solutions over clever ones.

Guidelines

Prefer	Avoid
Simple conditionals	Complex regex for simple checks
Explicit code	Magic numbers/strings
Standard patterns	Clever shortcuts
Direct solutions	Over-abstracted layers

Python Example

# Bad: Overly clever one-liner
users = [u for u in (db.get(id) for id in ids) if u and u.active and not u.banned]

# Good: Clear and readable
users = []
for user_id in ids:
    user = db.get(user_id)
    if user and user.active and not user.banned:
        users.append(user)

Rust Example

// Bad: Unnecessary complexity
fn process(data: &[u8]) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
    data.iter()
        .map(|&b| b.checked_add(1).ok_or("overflow"))
        .collect::<Result<Vec<_>, _>>()
        .map_err(|e| e.into())
}

// Good: Simple and clear
fn process(data: &[u8]) -> Result<Vec<u8>, &'static str> {
    let mut result = Vec::with_capacity(data.len());
    for &byte in data {
        result.push(byte.checked_add(1).ok_or("overflow")?);
    }
    Ok(result)
}

YAGNI (You Aren't Gonna Need It)

Principle: Don't implement features until they are actually needed.

Guidelines

Do	Don't
Solve current problem	Build for hypothetical futures
Add when 3rd use case appears	Create abstractions for 1 use case
Delete dead code	Keep "just in case" code
Minimal viable solution	Premature optimization

Python Example

# Bad: Premature abstraction for one use case
class AbstractDataProcessor:
    def process(self, data): ...
    def validate(self, data): ...
    def transform(self, data): ...


class CSVProcessor(AbstractDataProcessor):
    def process(self, data):
        return self.transform(self.validate(data))


# Good: Simple function until more cases appear
def process_csv(data: list[str]) -> list[dict]:
    return [parse_row(row) for row in data if row.strip()]