My thoughts:
I propose OOP (class-based, C++, Java, etc.) became popular (more so than FP) because of these DX concerns:
Intuitiveness: humans typically objectify the world (objectification, essentialism). GUI's could naturally be described in OO terms.
Accessibility: It made access/namespacing easy: if you had an object, you could easily and automatically find its behaviors/methods (autocomplete!). It provided even easier (more accessible and natural) modularization than modules with functions.
Domain modeling: It enabled domain modeling (in the language of objects in the domain). Aligned with GUI development.
Organisation: It facilitated code organisation and organisational compartmentalization (coupled behavior to data and encapsulated it). "You are responsible for those things" (vs. the better: "you are responsible for those flows / use cases"). Encapsulation made teams able to refactor internals while the interface/API remained a stable contract (enabling parallel development).
Performance: It allowed mutation (merged with imperative/procedural), which didn't compromise on performance (in perf constrained environments and popular applications like games etc.)
Predictability and reuse: Classes provided a clear interface/template for data, which made handling similar kinds of data more predictable and reusable (inheritance and polymorphism).
Perplexity.ai answer:
Object-oriented programming (OOP) became popular from a developer experience (DX) perspective because it made code more modular, reusable, and maintainable, which are all critical for effective teamwork and long-term project growth. The ability to encapsulate data and abstract away complexity led to simplified development, easier code updates, and clearer project organization.1 7 8
Core DX Benefits of OOP
Modularity: OOP lets developers organize code into distinct, logical units (objects), helping teams work on separate pieces independently without interfering with each other's work.7 1
Reusability: Features like inheritance and polymorphism allow programmers to reuse code, reducing repetition and saving time across projects.6 1
Maintainability: With encapsulation, internal details are hidden, making bugs easier to locate and fix while protecting sensitive data from unexpected changes.1
Abstraction: Developers interact with objects through clear interfaces, reducing the cognitive load and making it simpler to teach, understand, and extend codebases.1
Real-World Modeling: OOP enables clearer modeling of real-world concepts, making complex domains easier to translate into software structures.3
Why Developers Chose OOP
Scalability: Applications built with OOP could more easily adapt to growing requirements and changes, speeding up both feature development and troubleshooting.7 1
Team Collaboration: OOP’s structure facilitated parallel development, letting larger teams contribute without merging conflicts or breaking core functionality.1
Industry Adoption: The prevalence of popular OOP languages (Java, C++, Python) further reinforced its spread, as developers preferred tools with strong IDE, library, and community support.5
GUI and Event-Driven Programming: As user interfaces became central, OOP patterns fit naturally for modeling buttons, menus, and interactive elements.3
These factors combined to make OOP a dominant paradigm, not only for technical reasons but also because it dramatically improved how developers experienced, managed, and scaled software projects.8 7 1
Why did FP not become as popular as OOP (from a DX perspective)?
Functional programming (FP) has not achieved the same mainstream popularity as object-oriented programming (OOP) from a developer experience (DX) perspective due to key challenges around approachability, mental models, and ecosystem adoption. [1] [4] [5]
Challenges for FP Adoption
- Steeper Learning Curve: FP relies on abstract concepts like pure functions, immutability, and recursion, which are less intuitive for many developers, especially beginners. OOP's object-based approach more closely mirrors how people think about real-world entities, making it easier for teams to learn and collaborate. [4] [1]
- Mental Model Differences: FP separates data from behavior and avoids state changes. While this leads to safer and more predictable code, it can complicate scenarios involving shared state, like working with files, databases, or real-time data streams, which often require side effects. [2] [1]
- Tooling and Ecosystem: OOP languages such as Java, C++, and Python dominated industry adoption, offering richer IDE support, better documentation, and large communities, which further improved DX. FP languages often lacked these advantages in mainstream business contexts. [5]
- Readability and Maintainability: While FP offers concise code, excessive use of higher-order functions and recursion can make code harder to read and debug for those unfamiliar with the paradigm, leading to slower onboarding and more confusion for teams. [1] [4]
- State and Mutability Needs: Many practical business applications require direct management of state and mutable data. OOP’s encapsulation and state-handling patterns are often more effective and familiar for developers building GUIs, games, and large systems with complex interactions. [5] [1]
Key DX Differences: FP vs OOP
| Aspect | OOP (DX) Advantage | FP (DX) Challenge |
|---|---|---|
| Learning & Onboarding | Easy to explain objects/entities [4] | Abstract maths concepts (recursion, immutability) [4] [1] |
| Code Organization | Modular, extensible codebases [1] | Compact but may be harder to maintain [4] |
| Community & Tooling | Larger libraries, better IDE support [5] | Smaller, academia-focused communities [5] |
| State Handling | Direct and familiar (classes/fields) [5] | Avoids mutation, harder with shared state [2] |
| Performance & Memory | Efficient for complex, mutable systems [1] | Overhead from immutability [5] |
These factors made FP less attractive for DX in most mainstream software development contexts, even though it excels in niche domains such as data transformation and concurrent processing. [4] [1] [5]
How could a new FP language have at least as good DX as OOP without being OOP?
A new functional programming (FP) language could achieve developer experience (DX) at least as good as object-oriented programming (OOP) without actually becoming OOP by focusing on several key design principles and usability enhancements. [1] [2] [4]
Essential Features for Superior FP DX
- Intuitive Syntax and Ergonomics: Adopt clean, readable syntax that lowers the cognitive barrier for newcomers, with familiar constructs for both beginners and experts. [4]
- Rich Tooling and IDE Support: Provide high-quality debuggers, static analysis, code completion, and refactoring tools, matching the ecosystem strengths of OOP languages. [1]
- Explicit and Powerful Data Modeling: Offer strong, expressive types, pattern matching, and algebraic data types so that developers can model business domains naturally, much like classes/objects in OOP, but with immutable data. [1]
- Clear State Management: Include ergonomic mechanisms (perhaps via monads, effects, or built-in state-management utilities) to handle state and side effects in a way that feels intuitive and does not require advanced theory for daily use. [1]
- First-Class Modules and Composition: Enable modular development via first-class modules, namespacing, and function composition, allowing team collaboration and code reuse with clear boundaries, similar to OOP packaging and interfaces. [2]
- Concise Interop and Practical Libraries: Prioritize interoperability with existing systems and provide rich standard libraries for common tasks (GUI, database, networking), helping developers immediately solve practical problems. [1]
- Declarative UI and Event Handling: Make building user interfaces and event-driven programs as seamless as in OOP by using pure, compositional approaches (e.g., Elm, React-style architectures). [1]
- Strong, Accessible Documentation: Invest in clear guides, tutorials, and onboarding resources that demystify functional concepts and patterns for developers from OOP backgrounds. [1]
Key Principles to Emphasize
- Pure functions for predictability and easy testing. [2] [1]
- Immutability to prevent certain classes of bugs, with simple, ergonomic data updating. [1]
- Higher-order functions and composition for modularity, without unnecessary academic jargon. [2] [1]
- Declarative style for clarity and maintainability. [1]
- Built-in support for recursion, effect management, and concurrency that feels natural in large-scale application development. [1]
By proactively addressing usability, approachability, and real-world application integration—while keeping FP's strengths—such a language could offer an excellent DX that rivals, and even surpasses, traditional OOP environments without relying on OOP constructs. [4] [2] [1]
Why necessarily "first-class modules"?
First-class modules are necessary in a new FP language aiming for excellent DX because they allow the modular organization and flexible composition of code at runtime, without adopting OOP’s class-based approach. This capability helps teams create scalable, maintainable, and collaborative projects by treating modules as values that can be passed, stored, and dynamically selected—much like functions in FP, but for entire feature sets or data models (3, 6).
Why First-Class Modules Matter in FP
Modularity without OOP: Unlike OOP, which uses classes and objects to encapsulate state and behavior, FP relies on composition and separation of concerns. First-class modules provide an alternative by allowing groups of related functions, types, and data to be packaged together and manipulated as units, increasing code reuse and adaptability (6, 3).
Dynamic Composition: Developers can pass modules to functions, store them in data structures, or swap implementations at runtime. This flexibility enables plugin architectures, runtime configuration, and testing scenarios that mimic OOP’s extensibility, but stay in the FP paradigm (3, 6).
Type Safety and Abstraction: First-class modules can encapsulate type definitions and provide strong abstraction boundaries, supporting safe, predictable evolution of codebases as requirements change (7, 3).
Collaboration and Scaling: Large teams benefit from clear modular boundaries, enabling parallel development and easier code management—a core DX advantage in any mature language ecosystem (6, 3).
Thus, first-class modules help FP languages deliver the same DX strengths as OOP—modularity, organization, extensibility, and collaboration—without relying on objects, and fit naturally within the functional paradigm (7, 3, 6).
Top comments (1)
First-class modules (used by OCaml and 1ML) are considered incompatible with Type classes (used by Haskell, and also the statically typed Lisp-language called Coalton).
Rust creator Graydon Hoare considers 1ML as the pinnacle of module systems, since it simplifies first-class modules and unifies the module system with the language itself in a way that is both powerful and intuitive.