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How can you improve your software engineering skills by refactoring data structures?

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Refactoring data structures is a software engineering practice that involves changing the way data is organized, stored, and accessed in a program, without altering its functionality or behavior. Refactoring can improve the readability, maintainability, performance, and scalability of your code, as well as reduce errors and bugs. In this article, you will learn how to improve your software engineering skills by refactoring data structures, and what benefits and challenges you may encounter along the way.

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Chiamaka Umeh

Frontend Engineer | MERN Stack & Graph ql | Next js | React Js | Redux | Svelte | Typescript | Node JS | AWS | UK…
ehsan ozeyri

PhD Student, Tech Lead Manager, Senior Staff Engineer, Project Manager, Entrepreneur, Inventor
Marcos N. Santana

Transforming Software Excellence | Visionary Leader in Software Engineering and Quality Engineering | 21+ Years Shaping…

1 What are data structures?

Data structures are the building blocks of any software program, as they define how data is stored and manipulated in memory. Data structures can be simple, such as arrays, lists, stacks, or queues, or complex, such as trees, graphs, hash tables, or heaps. Data structures have different properties and operations, such as size, capacity, access time, insertion, deletion, sorting, searching, or traversal. Choosing the right data structure for your problem and data set is crucial for the efficiency and quality of your code.

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2 Why refactor data structures?

Refactoring data structures can help you in many ways, such as simplifying your code and making it easier to read, reducing duplication and redundancy of data and code, optimizing memory and CPU resources, enhancing modularity and reusability, adapting to changing requirements and new features, improving testability and reliability, and avoiding potential errors caused by data inconsistency or corruption.

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Chiamaka Umeh

Frontend Engineer | MERN Stack & Graph ql | Next js | React Js | Redux | Svelte | Typescript | Node JS | AWS | UK Global Talent
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I don't see refactoring data structures as just a technical exercise; it's a key to understanding the full potential of your coding skills. As a developer, I've come to understand that refining data structures leads to cleaner, more maintainable code. Refactoring data structures can be likened to giving your code a makeover, to ensure that it remains agile and adaptable to evolving requirements.

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ehsan ozeyri

PhD Student, Tech Lead Manager, Senior Staff Engineer, Project Manager, Entrepreneur, Inventor
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Refactoring data structures enhances your code across several dimensions: Clarity: Simplifies and declutters, removing redundancy. Performance: Optimizes the use of memory and CPU. Consistency: Reduces errors through uniform data handling. Modularity: Increases component reuse and flexibility. Future-Proofing: Readies code for upcoming changes and features. Reliability: Improves testability, leading to more dependable code. These improvements are crucial for maintaining high-quality, adaptable software in a rapidly changing technology landscape. Also refactoring data structures is about sharpening the developer’s approach to coding for better performance and future readiness.

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Marcos N. Santana

Transforming Software Excellence | Visionary Leader in Software Engineering and Quality Engineering | 21+ Years Shaping Future Tech
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Refactoring for the betterment of the code is always the ideal approach, hence why these exercises are denominated as Kaizens. The term comes from “Kai”, which means “change”, and “Zen”, which means “good” or “for better”. With data structures, it’s even more important to maintain coherence between the application that consumes it, and therefore essential for its scalability to refactor them whenever necessary.

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3 How to refactor data structures?

Refactoring data structures is not a one-size-fits-all process, but rather a context-dependent and iterative one. To begin, you must analyze your code and identify the data structures that need refactoring. The common steps and methods for refactoring data structures include reviewing your code and data flow to determine the purpose and scope of each data structure, evaluating performance and suitability to identify any issues or bottlenecks, choosing a new or alternative data structure that better fits your needs and goals, implementing the new structure in your code and updating any references or dependencies, testing your code to verify that functionality and behavior remain unchanged after refactoring, and repeating the process until you are satisfied with the results.

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4 What are some examples of refactoring data structures?

When refactoring data structures, the type, complexity, and context of your code should be taken into consideration. For instance, if you need to add or remove elements frequently, you might replace an array with a list. Alternatively, if you need to access elements by a key or a value, you might replace a list with a hash table. Moreover, if you need to represent complex relationships between data nodes or perform advanced algorithms or traversals, you could replace a tree with a graph. Finally, if you want to leverage existing functionality and performance of the library or avoid reinventing the wheel, you could replace a custom data structure with a standard library one.

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Marcos N. Santana

Transforming Software Excellence | Visionary Leader in Software Engineering and Quality Engineering | 21+ Years Shaping Future Tech
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In my case, I always tend to favor creating data structures for consuming a list of records with List or List<T> whenever possible for standardization purposes.

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5 What are the benefits and challenges of refactoring data structures?

Refactoring data structures can bring many benefits to your software engineering skills and code quality, such as improving your problem-solving and analytical skills, design and coding skills, performance and scalability skills, as well as testing and debugging skills. However, refactoring data structures can also pose some challenges, such as introducing new errors or bugs, breaking existing functionality or behavior, increasing complexity or the learning curve, or taking too much time or effort. Therefore, you need to be careful and systematic when refactoring data structures, and use the appropriate tools and techniques to facilitate and automate the process.

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Marcos N. Santana

Transforming Software Excellence | Visionary Leader in Software Engineering and Quality Engineering | 21+ Years Shaping Future Tech
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Careful consideration must always be taken when attempting to persist data that is consumed in-memory. That is one of the costliest mistakes made in Software Engineering, as sometimes the sacrifice is made to persist records in a database and it hinders the performance / behavior of the system significantly.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you improve your software engineering skills by refactoring data structures?

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1 What are data structures?

2 Why refactor data structures?

3 How to refactor data structures?

4 What are some examples of refactoring data structures?

5 What are the benefits and challenges of refactoring data structures?

6 Here’s what else to consider

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