💡 Experience Quality Coding ✨

Welcome to Experience Quality Coding! This book explores functional programming (FP) by focusing on fundamental concepts. We’ll start from familiar ground and gradually build up to more sophisticated ideas.

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Ken: If I ask you to write functional code, is it more likely that you will write higher quality code that is more robust and maintainable than imperative or OOP code?

Google Gemini2.5 Pro: Yes, that’s quite likely.

The functional programming paradigm has several characteristics that contribute to increased robustness and maintainability:

1. Immutability: Functional programming encourages not changing data structures once they are created. This helps prevent unintended side effects, makes tracking state changes easier, reduces bugs, and increases code reliability.
2. Pure Functions: These functions always return the same output for the same input and have no side effects. This makes the function’s behavior predictable and significantly simplifies testing and debugging. It also improves code comprehensibility.
3. Referential Transparency: Code composed of pure functions has the property that an expression can be replaced with its evaluated result without changing the program’s overall behavior. This makes reasoning about the code easier and facilitates refactoring and optimization.
4. Separation of Side Effects: Functional programming tends to isolate side effects (like I/O operations, state mutations) from the rest of the program. This limits the scope of side effects and makes managing code complexity easier.

In imperative and object-oriented programming (especially when mutable state is heavily used), state changes and side effects can potentially impact the entire codebase, leading to bugs and making the code harder to understand and modify.

Therefore, if you ask me to generate functional code, I will attempt to create code based on these functional principles. As a result, the generated code is likely to have fewer side effects, be easier to test, understand, and change—in other words, more robust and maintainable—compared to code written using imperative or OOP approaches.

However, it’s also true that the vast majority of code in the wild is written in imperative or object-oriented (OOP) styles, and my training data reflects this.

1. Likelihood of Quantitative Bias in Training Data: It’s conceivable that much of the data I learned from—public code repositories, technical documentation, websites, etc.—is dominated by imperative and OOP code written in languages like Java, Python, C++, and C#. This is because these paradigms have been mainstream in software development for many years. Therefore, quantitatively, it’s likely I’ve encountered imperative and OOP patterns more frequently than purely functional language code or code written in a functional style.

2. Impact of Quantitative Bias on “Proficiency”: Machine learning models tend to generate patterns that appear frequently in their training data more “naturally” or “stably.” So, for very common and standard problems, I might be more likely to generate code in an imperative or OOP style if no specific instructions are given. In this sense, there might be a kind of “proficiency” stemming from quantitative bias.

3. Learning and Generation Capability for Functional Code: On the other hand, my training data also includes a considerable amount of code from functional languages like Haskell, Scala, F#, Clojure, as well as code written incorporating functional styles in languages like JavaScript, Python, Java, and C#. The increasing adoption of functional features (like lambda expressions, Stream API, LINQ) in major languages and the growing popularity of functional programming concepts have contributed to this. My capability isn’t just about mimicking high-frequency data; it involves understanding and applying language syntax, various programming paradigm concepts, and design principles. Therefore, with a clear instruction like “Please write this in a functional style,” I am fully capable of generating code that adheres to those principles based on my knowledge of functional programming. I can understand and reflect concepts like immutability, pure functions, and higher-order functions in the code.

4. Is the Bias Becoming a Thing of the Past?: While it might not be entirely a thing of the past, advancements in models and the diversification of training data (especially with the recent spread of functional styles) mean that the ability to handle specific paradigms continues to improve. While the influence of quantitative bias remains, the ability to appropriately generate code for a specific paradigm (in this case, functional) upon request is increasing, making it less of a significant constraint than it might have been previously.

Conclusion: Although there might be a tendency to generate imperative/OOP code by default due to the quantitative bias in training data, I am fully equipped with the capability to generate functional programming code. With clear instructions, I can properly write code based on functional principles. Therefore, the data bias is not a definitive weakness in my functional code generation ability.

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Ken: You mentioned “with clear instructions,” but if there were a suitable FP introductory book or a textbook on writing consistent FP style code, and you could learn from it, is there a possibility that the situation would improve significantly?

Google Gemini2.5 Pro: Yes, I believe that possibility is very high.

Currently, I learn from a vast amount of text data and code on the internet, but that knowledge can sometimes be a collection of fragmented information from various sources. Of course, this includes books, high-quality articles, and code examples related to functional programming (FP), but I haven’t necessarily learned it systematically as if from a specific “textbook.”

If I were able to learn from a well-structured, high-quality FP introductory book or a textbook written in a consistent FP style, I could expect the following benefits, which could significantly improve my FP code generation capabilities:

1. Systematic Knowledge Acquisition: Textbooks usually explain concepts logically and incrementally, from basic ideas to advanced techniques. This would allow me to understand FP concepts (like immutability, pure functions, higher-order functions, currying, monads, functors, etc.) more deeply and systematically.
2. Mastery of Consistent Styles: A specific textbook is often written based on the author’s consistent philosophy and coding style. Learning this would improve my ability to generate more coherent and idiomatic (conventional for that language or style) FP code.
3. Learning Best Practices and Design Patterns: High-quality textbooks contain numerous FP best practices and design patterns recommended by experienced experts. Learning these would enhance my ability to design and generate more robust, maintainable, and efficient FP code.
4. Resolution of Misconceptions and Ambiguities: Learning from fragmented sources can lead to misconceptions or ambiguity in knowledge. Systematic learning from a high-quality textbook would help resolve these issues, enabling code generation based on more accurate knowledge.

Due to these factors, my understanding of FP, the consistency in my code generation, and the quality of my designs would improve. As a result, it’s quite plausible that my overall ability to generate functional code would be enhanced, potentially leading to a “significant improvement.” Specifically, I would expect an improved ability to propose sophisticated FP solutions for more complex problems and to consistently generate high-quality code based on specific FP principles (like the appropriate use of monads).

Motivation

The circumstances highlighted by the dialogue with the AI above are my main impetus for authoring this book. Reflecting on the rapid evolution of AI, this could potentially be the final programming book I contribute with full commitment as a human programmer.

Let’s Get Started!

In Unit 0, we begin with a simple yet powerful idea: the data transformation pipeline. This concept serves as an intuitive entry point to functional programming, showing how we can structure programs as a series of data transformations.