Software Modularity: Trivial Concept, Yet Still Rarely Done Right!

If you’ve ever read any software engineering blog or book, you’ve probably seen the word “Modularity” mentioned many times. But ask most engineers what modularity really means, what benefits it brings, and how to actually break a system into modules — and you’ll often get vague or unclear answers.

To find clear and timeless answers, we need to go back to the 1960s and 70s, when these ideas were first introduced and refined. In his famous 1972 paper (over 8,000 citations and still cited 250+ times in 2025!), David Parnas tackled a problem that was unsolved until then: the criteria for decomposing software into modules.

Parnas identifies three key benefits of correct modularization:

🤑 Managerial: Breaking software into modules enables teams to work in parallel without blocking each other — that’s modern-day agility.

🫤 Flexibility: By isolating changes inside modules, we avoid ripple effects and can evolve the product easily — what we now call evolveability or modifiability.

🤷‍♂️ Comprehensibility: A modular system can be understood piece by piece, instead of trying to grasp the whole system at once — improving understandability.

But what’s the right way to split modules? 🤔

Parnas points out that many engineers (then and surprisingly even today) design modules based on flowcharts or the sequence of operations. This might work for small codebases (~5,000–10,000 lines), but it fails badly as systems grow larger.

To build maintainable and extensible systems, Parnas recommends his concept of Information Hiding:

Each module should hide the knowledge about design decisions it encapsulates — like specific data structures, APIs, or protocols — behind clear abstractions. Modules are not defined by the order they’re called but by what information they hide from each other.

Parnas shows how poor modularization leads to rigid software, while proper modularization builds flexible, evolving systems. To fully understand these concepts, especially through his clear example, you should read the paper itself. Sometimes, going back to basics and revisiting foundational ideas is exactly what we need — and this classic paper delivers that perfectly.