SOLID Principles for Scientists and Engineers: Making Research Code Maintainable
Source: Dev.to
Why Your Research Code Needs Better Design
It starts innocently enough: a 200‑line Python script that analyzes experimental data works perfectly, and your advisor is happy. Six months later the script has ballooned to 2,000 lines, supporting multiple analysis methods, several instrument types, and a plotting system that “mostly works.” Every new feature risks breaking something else, and the only person who understands the code is you—still not entirely sure how it all fits together.
You know the code needs restructuring, but where do you start? Design patterns and software architecture resources often target web applications and enterprise systems, not spectroscopy data analysis or lab‑equipment control. This series is for you.
What This Series Covers
Over the next few weeks I’ll publish a 5‑part series on the SOLID principles—a set of design principles that make code more maintainable, testable, and extensible. Each principle gets its own detailed post with complete, runnable Python examples drawn from real scientific scenarios (no shopping carts, no user authentication).
The Five Principles
| Principle | Tagline | Typical Scientific Use‑Case |
|---|---|---|
| Single Responsibility Principle | “One Class, One Job” | Separate data loading, analysis, and visualization. |
| Open/Closed Principle | “Extending Without Breaking” | Add new analysis methods without modifying existing code. |
| Liskov Substitution Principle | “Interchangeable Components” | Swap instrument drivers or file parsers safely. |
| Interface Segregation Principle | “Lean Interfaces” | Provide only the methods each component actually needs. |
| Dependency Inversion Principle | “Depend on Abstractions” | Decouple high‑level pipeline logic from low‑level hardware details. |
Each post includes:
- A relatable scientific problem.
- “Before” code that illustrates the issue.
- “After” code that applies the principle.
- An explanation of why the refactoring helps.
- Guidance on when to apply (and when to skip) the principle.
Who Should Read This
- PhD students whose “temporary” script now supports multiple lab members.
- Research scientists maintaining code that has outgrown its original scope.
- Engineers who dread opening a six‑month‑old codebase.
- Lab managers tasked with maintaining code written by departed students.
You don’t need a computer‑science degree or knowledge of design patterns. You just need code that’s becoming harder to maintain.
How to Recognize When to Apply SOLID
| Development Stage | Typical Need | SOLID Intensity |
|---|---|---|
| Exploratory code | Quick data exploration | No SOLID needed |
| One‑off script | Simple analysis | Light SOLID |
| Production pipeline (runs daily for years) | Reliable, repeatable processing | Moderate SOLID |
| Reusable library (used by a whole research group) | Extensible, testable, maintainable | High SOLID |
Red flags that indicate it’s time to refactor:
- Changing how data is loaded forces edits in the same file that performs analysis.
- Adding a new feature breaks unrelated functionality.
- Swapping one component for another produces strange results.
- Classes contain methods that only raise
NotImplementedError. - Tests require real hardware or actual files to run.
If you answered “yes” to any of these, the upcoming posts will show you exactly what to do.
Series Schedule and What to Expect
- Frequency: Weekly, starting next Monday.
- Reading time: Approximately 15–20 minutes per post.
- Content: Problem description, before/after code, refactoring rationale, and applicability guidance.
First post (Monday): Single Responsibility Principle for Scientists and Engineers – a realistic spectroscopy analysis script will be refactored step‑by‑step to illustrate how to break monolithic code into focused, maintainable pieces.
Getting Started
Take a look at your current codebase and ask yourself:
- When I need to change how I load data, do I have to edit the same file that does the analysis?
- When I add a new feature, do unrelated things break?
- Have I ever swapped one component for another and gotten strange results?
- Do I have classes implementing methods that just raise
NotImplementedError? - Can I test my code without connecting to real hardware or reading real files?
If any answer is “yes,” the series will give you concrete steps to improve your code.
Feel free to leave comments with questions or topics you’d like covered. Happy coding!