Memoization is awesome. Let's abuse it. TL;DR: Don't apply premature optimization too early Problems Readability
Code Complexity
Premature Optimization
Obscured Logic Solutions Apply memoization in actual real business situations, and measure its impact through empirical benchmarks. Context Memoization can help you improve the performance of recursive functions involving redundant computations but compromise code readability and maintainability. It would help if you only used it with strong factual evidence on real business case scenarios. Sample Code Wrong memo = {}
def factorial_with_memo(n):
    if n in memo:
        return memo[n]
    if n == 0:
        return 1
    result = n * factorial_with_memo(n-1)
    memo[n] = result
    return result
  
  # This function optimizes the computation of factorials
  # by storing previously computed values,
  # reducing redundant calculations 
  # and improving performance for large inputs. Right def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1) Detection [x]Semi-Automatic You can search for all places where you are using this technique and validate if they are worth it. Exceptions Real performance problems with strong factual evidence Tags Premature Optimization Level [x]Intermediate AI Generation Unless you explicitly ask the IAs to use this technique, they will suggest cleaner solutions. AI Detection ChatGPT, Gemini, and Claude.ai detect some problems with this technique but do not mention readability as a concern. Conclusion It would be best if you kept a balance between performance optimization and code clarity. You can consider alternatives such as iterative approaches or algorithmic optimizations since memoization significantly compromises code readability. Relations Code Smell 06 - Too Clever Programmer Code Smell 20 - Premature Optimization More Info Wikipedia Disclaimer Code Smells are my opinion. Credits Photo by Steffen Lemmerzahl on Unsplash A cache with a bad policy is another name for a memory leak. Rico Mariani Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code Memoization is awesome. Let's abuse it. Memoization is awesome. Let's abuse it. TL;DR: Don't apply premature optimization too early TL;DR: Don't apply premature optimization too early premature optimization Problems Readability Code Complexity Premature Optimization Obscured Logic Readability Code Complexity Premature Optimization Obscured Logic Solutions Apply memoization in actual real business situations, and measure its impact through empirical benchmarks. Apply memoization in actual real business situations, and measure its impact through empirical benchmarks. memoization Context Memoization can help you improve the performance of recursive functions involving redundant computations but compromise code readability and maintainability. It would help if you only used it with strong factual evidence on real business case scenarios. strong factual evidence real Sample Code Wrong memo = {}
def factorial_with_memo(n):
    if n in memo:
        return memo[n]
    if n == 0:
        return 1
    result = n * factorial_with_memo(n-1)
    memo[n] = result
    return result
  
  # This function optimizes the computation of factorials
  # by storing previously computed values,
  # reducing redundant calculations 
  # and improving performance for large inputs. memo = {}
def factorial_with_memo(n):
    if n in memo:
        return memo[n]
    if n == 0:
        return 1
    result = n * factorial_with_memo(n-1)
    memo[n] = result
    return result
  
  # This function optimizes the computation of factorials
  # by storing previously computed values,
  # reducing redundant calculations 
  # and improving performance for large inputs. Right def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1) def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1) Detection [x]Semi-Automatic [x] Semi-Automatic [x] You can search for all places where you are using this technique and validate if they are worth it. Exceptions Real performance problems with strong factual evidence Real performance problems with strong factual evidence Tags Premature Optimization Premature Optimization Level [x]Intermediate [x] Intermediate [x] AI Generation Unless you explicitly ask the IAs to use this technique, they will suggest cleaner solutions. AI Detection ChatGPT, Gemini, and Claude.ai detect some problems with this technique but do not mention readability as a concern. Claude.ai Conclusion It would be best if you kept a balance between performance optimization and code clarity. You can consider alternatives such as iterative approaches or algorithmic optimizations since memoization significantly compromises code readability. Relations Code Smell 06 - Too Clever Programmer Code Smell 06 - Too Clever Programmer Code Smell 20 - Premature Optimization Code Smell 20 - Premature Optimization More Info Wikipedia Wikipedia Disclaimer Code Smells are my opinion . opinion Credits Photo by Steffen Lemmerzahl on Unsplash Steffen Lemmerzahl Unsplash A cache with a bad policy is another name for a memory leak. A cache with a bad policy is another name for a memory leak. Rico Mariani Rico Mariani Software Engineering Great Quotes Software Engineering Great Quotes This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

How to Find the Stinky Parts of Your Code: 251 - Empty Collections 

How to Find the Stinky Parts of Your Code: 249 - Constants as Numbers

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Premature Memoization: How to Properly Apply It - Code Smell 250

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