Raleigh MacRory
|Subscribers
About
First Steroid Cycle: Best Steroids For Muscle Growth Before And After Result, Steroids For Beginners By CrazyBulk USA
The "Recipe" for Quality Code
(think of it as your favorite cookbook—only instead of flour and sugar we’re mixing readability, maintainability, and reliability.)
Step What you do Why it matters Quick example
1. Start with a clear purpose Write a one‑sentence description or comment that explains why the code exists. Helps anyone (including future you) instantly see what problem is being solved, saving hours of guesswork. `// Calculates the tax amount for a given purchase.`
2. Keep functions small & focused Aim for 1–5 lines per function, or no more than ~50 LOC if it can’t be split further. Small units are easier to test, debug, and reuse. ```cpp
int add(int a, int b) return a + b;
```
3. Use meaningful names Variables, functions, types should describe intent (e.g., `taxRate` vs. `x`). Reduces the cognitive load when reading code later. `double tax = purchasePrice taxRate;`
4. Avoid side‑effects where possible Pure functions return results without modifying global state or I/O. Predictable behavior simplifies reasoning about programs. ```cpp
int factorial(int n)
if (n <= 1) return 1;
return n factorial(n-1);
```
5. Keep functions small One function should do one thing; no more than a few dozen lines. Easier to test, reuse, and maintain. `bool isPrime(int x);` (separate from printing results)
6. Use descriptive names Variables, parameters, and functions clearly indicate intent. Reduces the need for comments or external documentation. `int computeTaxableIncome(const Salary& s);`
7. Prefer const where possible Immutable objects reduce accidental state changes. Makes code safer and easier to reason about. `void printSalary(const Salary& salary) const;`
8. Keep the main function minimal The entry point should orchestrate, not implement logic. Simplifies unit testing of components. `int main() Salary s = getUserInput(); process(s); `
2.4 Example: Refactoring a Class
Suppose we have an old class:
class Salary
public:
double salary;
double tax_rate;
double net_salary()
return salary (1 - tax_rate);
;
Refactored version with proper encapsulation and immutability:
class Salary
private:
const double gross_;
const double taxRate_;
public:
Salary(double gross, double taxRate)
: gross_(gross), taxRate_(taxRate) {}
double net() const return gross_ (1.0 - taxRate_);
;
Here:
The fields are `const`, making the object immutable after construction.
Getter methods (`net`) provide read-only access.
No setters; the state cannot change once created.
4. Summary of Key Findings
Issue Description Impact
Unclear Naming Method `add` uses ambiguous names (`c`, `d`). Makes code hard to read and maintain.
Null Checks & Exceptions No validation for null arguments or potential overflow. Leads to runtime errors in production.
Incorrect Exception Type Throws `NullPointerException` on empty stack. Misleads developers; should be `IllegalStateException`.
Inefficient Data Structures Using `ArrayList` with frequent inserts at the end and deletions from middle is costly (O(n)). Poor performance, especially for large stacks.
Unnecessary Synchronization Synchronized methods lock whole object, causing contention. Reduces concurrency without clear benefit.
---
4. Suggested Improvements
Area Current Issue Proposed Solution
Data Structure `ArrayList` + `HashMap`. Use a linked list (e.g., `java.util.LinkedList`) or a custom node structure: each element stores its value and pointers to next/prev nodes.
Memory Efficiency 2 arrays of size `maxSize`. Allocate only what’s needed; use dynamic structures that grow as required.
Time Complexity O(1) for add, but remove is O(log n). With linked list, removal by value is still O(n); consider maintaining a hash map from values to nodes to achieve O(1) removal.
Synchronization `synchronized` methods. Use `java.util.concurrent.locks.ReentrantLock` for finer-grained control; or lock-free structures if needed.
Size Tracking Separate `size` field. Keep a single atomic counter (e.g., `AtomicInteger`) for thread-safe size updates.
3.2 Suggested Optimizations
Hash Map of Value to Node:
- Store each element’s node reference in a hash map keyed by the element value.
- Removal becomes O(1) by looking up the node directly, then unlinking it from the doubly linked list.
Circular Doubly Linked List with Sentinel Head:
- Simplify edge cases for insertion and deletion.
- Avoid null checks for head/tail pointers.
Atomic Operations for Size:
- Use `AtomicInteger` to maintain size without locking.
Lock-Free Traversal:
- If the data structure is used in a concurrent environment, design it so that read operations do not acquire locks (e.g., using immutable snapshots or copy-on-write).
Memory Management:
- In languages like Java/C#, rely on garbage collection.
- In manual memory management environments, ensure proper deallocation of nodes.
---
7. Summary
Node Removal: Deleting a node requires updating its predecessor’s `next` pointer and, if the node is not the tail, its successor’s `prev` pointer (or adjusting the head/tail references).
Tail Node Handling: When removing the last node, the list becomes empty; update both head and tail to null.
Complexity: The operation runs in constant time (`O(1)`) because it involves only a few pointer updates.
Edge Cases: Must handle removal of the sole node, the first node, the last node, or any middle node correctly.
Memory Management: In languages with manual memory management, free the node after detaching; otherwise rely on garbage collection.
By adhering to these principles and carefully updating all necessary references, one can safely remove a tail node from a doubly linked list while preserving its structural integrity.
