Writing Clean and Maintainable MATLAB Code

Why Clean Code Matters in MATLAB

Clean and maintainable MATLAB code is easier to read, easier to debug, and easier to extend in the future. You will often return to your own scripts and functions weeks or months later. If the code is written clearly, you can understand what it does in minutes instead of hours, and other people can understand it too. In practice, writing clean code saves time, reduces mistakes, and makes debugging far less painful.

In this chapter the focus is on habits and style, not on new language features. The same MATLAB constructs you already know can either be used in a clear way or a confusing way. The aim is to choose the clear way as consistently as possible.

Choosing Good Names

One of the most important parts of clean MATLAB code is the choice of names for variables, functions, and files. Names should describe what something represents, not how it is stored or a single letter from a formula, except in very short and obvious cases.

Instead of writing something like:

x = [1 2 3];
y = mean(x);

it is usually clearer to write:

temperaturesC = [18 20 19];
averageTemperatureC = mean(temperaturesC);

Use names that indicate units if relevant, such as speedKmPerH, timeSeconds, or voltageV. For functions, choose verbs or verb phrases like loadPatientData, computeSpectrum, or plotResults. This makes it clear what the function does when you see a call such as plotResults(dataTable).

Avoid generic names like data, temp, or val unless the context is very limited and temporary. Also avoid overly long names that are hard to type and remember. Aim for names that are descriptive but still concise.

Finally, keep function file names consistent with the main function they contain. If your function is:

function stats = summarizeTable(tbl)

then save it in a file named summarizeTable.m. This matches MATLAB’s expectations and makes your project easier to navigate.

Structuring Code into Small Pieces

Clean MATLAB code is usually organized into small, focused scripts and functions. A function should perform one clear task. If a function starts to grow and do many unrelated things, it becomes hard to read and to test.

If you see repeated blocks of code, or a long script that handles reading data, processing, plotting, and saving all in one place, consider splitting some of that logic into helper functions. For example, you might break a workflow into functions like readInputData, cleanInputData, analyzeData, and createSummaryPlots. Your main script can then look like a table of contents:

dataRaw   = readInputData("input.csv");
dataClean = cleanInputData(dataRaw);
results   = analyzeData(dataClean);
createSummaryPlots(results);

This structure is much easier to understand than a single long sequence of operations. It also allows you to test and debug each part separately.

Within a function, group lines that belong together and separate groups with blank lines. This visual structure helps the reader see the steps in your logic.

Using Comments Effectively

Comments should explain why the code exists, not simply restate what is already obvious. For example:

% Compute mean temperature per day
meanTemp = mean(dailyTemperatures);

is helpful, because it states the purpose. Writing

% Take mean
meanTemp = mean(dailyTemperatures);

adds almost nothing compared to the code itself.

Use comments to document assumptions, tricky parts, and any non obvious details. If you are using a formula from a paper, you might reference the source or write the formula in words:

% Use equation (3) from Smith (2022) for corrected signal:
% S_corrected = S_raw ./ (1 + alpha * S_raw).
signalCorrected = signalRaw ./ (1 + alpha * signalRaw);

You can also temporarily comment out lines while debugging, but avoid leaving large blocks of dead code in files. Once you have finished debugging and you are confident in the solution, remove commented out experiments to keep the file clean.

At the top of each function, use a short comment section to explain its purpose and basic usage. This header is used by MATLAB’s help command. For example:

function result = normalizeSignal(signal)
% NORMALIZESIGNAL Normalize a numeric vector to have mean 0 and standard deviation 1.
%   result = NORMALIZESIGNAL(signal) returns a vector with the same size as
%   signal, whose values have zero mean and unit standard deviation.
mu = mean(signal);
sigma = std(signal);
result = (signal - mu) ./ sigma;
end

This style of help text makes your functions self documenting and easier for others to adopt.

Indentation and Layout

Consistent indentation and layout make MATLAB code much easier to scan and understand. MATLAB automatically indents blocks inside if, for, and while statements, and the editor can fix indentation for a whole file.

Basic layout rules that help readability include:

Indent the body of if, for, while, and switch blocks by the same number of spaces or tabs throughout the project.

Place each statement on its own line instead of chaining many commands together separated by semicolons.

Align code in ways that emphasize structure, not only aesthetics. For instance, align the contents of a long if chain to show alternatives clearly.

You should also keep lines to a reasonable length. If a single expression becomes very long, break it across multiple lines, using ellipses:

y = a .* sin(x) ...
    + b .* cos(x) ...
    + c;

Place the ellipsis at the end of the line and align the next line to show continuation. This also makes debugging easier because MATLAB points to shorter, more manageable lines.

Avoiding Unnecessary Complexity

Clean and maintainable code avoids clever tricks that are hard to understand. While MATLAB allows many compact expressions, a very short line is not always clearer. For example, a nested expression like:

result = sum((x - mean(x)).^2) / (numel(x) - 1);

is acceptable and still readable, but much more complex versions with heavy nesting can be confusing. When an expression is hard to read, break it into steps with intermediate variables that have meaningful names:

xCentered = x - mean(x);
squared   = xCentered.^2;
result    = sum(squared) / (numel(x) - 1);

This approach can slightly increase the number of lines but usually makes the intention much clearer, which is better in the long run.

Also try to avoid deeply nested if or for structures. If you notice nested levels becoming difficult to follow, see whether you can reorganize the logic into helper functions or early returns from a function to simplify the structure.

Writing Reusable and Flexible Code

When you write MATLAB code, it is useful to think about how you might reuse it later. Simple changes can make functions more flexible without much extra effort.

One common approach is to use function parameters instead of hard coding numbers or file names. For instance, instead of:

data = readtable("input.csv");

you can write a function:

function data = loadData(fileName)
    data = readtable(fileName);
end

This lets you call loadData("experiment1.csv") or loadData("experiment2.csv") without editing the function itself.

Another approach is to provide optional arguments or sensible default values. Inside a function, you can test nargin to check how many inputs the user passed and choose defaults. For example:

function y = smoothSignal(x, windowSize)
% SMOOTHSIGNAL Smooth a vector with a moving average.
%   y = SMOOTHSIGNAL(x, windowSize) smooths x using the specified windowSize.
%   If windowSize is omitted, a default of 5 is used.
if nargin < 2
    windowSize = 5;
end
y = movmean(x, windowSize);
end

This pattern keeps the function simple to call and still adaptable to different needs.

Writing reusable code also includes avoiding unnecessary dependence on the current folder or on global state. Prefer to pass data into functions as inputs and get results as outputs, instead of reading and writing variables in the base workspace.

Using Vectorization Sensibly

MATLAB is designed to work efficiently with vectors and matrices. Vectorized code, which operates on whole arrays at once, is usually faster than loops and often shorter. However, extremely compact vectorized expressions can become hard to understand if you overuse them.

As a guideline, prefer clear vectorized operations for simple patterns such as applying a function to every element, using logical indexing to filter values, or computing summary statistics. For example:

positiveValues = x(x > 0);

is both readable and efficient.

If a vectorized expression becomes hard to parse, consider using a simple for loop with clear variable names. Modern MATLAB usually executes loops quickly, and readability is more important than saving a small number of lines. Choose the style that makes the logic easiest to understand for someone who reads the code later.

Defensive Coding and Simple Checks

Maintainable MATLAB code often includes simple checks that catch incorrect inputs early. These checks can prevent confusing error messages deeper in your code and make bugs easier to diagnose.

For important functions, consider verifying assumptions about inputs. For example, you can check that a vector is nonempty or that dimensions match requirements:

function meanValue = safeMean(x)
% SAFEMEAN Compute mean of a nonempty numeric vector.
if isempty(x)
    error("safeMean:EmptyInput", "Input vector x must not be empty.");
end
if ~isvector(x) || ~isnumeric(x)
    error("safeMean:InvalidInput", "Input x must be a numeric vector.");
end
meanValue = mean(x);
end

Using error with an identifier like "safeMean:EmptyInput" makes messages more informative and helps you trace where the problem occurred. You do not need to check every possible condition in every function, but a few targeted checks can prevent subtle bugs later.

Collaborating with MATLAB Tools

The MATLAB Editor includes several tools that support clean and maintainable code. The Code Analyzer highlights potential problems, such as unused variables or unreachable code, and suggests improvements in real time. Paying attention to these suggestions helps you keep files tidy.

When you see warnings in the editor margin, review them and decide whether to fix the issue or to adjust your code to make your intention clearer. Often, simple changes like removing unused variables, preallocating arrays, or renaming conflicting variables can remove warnings and improve clarity.

The editor can also automatically format and indent code. Use this feature regularly when you edit or refactor larger sections. Consistent formatting across your files reduces cognitive load and makes it easier to spot logical problems while reading.

Finally, using comments with the help style, organizing files into meaningful folders, and giving each function its own file work together with MATLAB’s search and documentation features. These habits turn your codebase into a small library that you and others can navigate and extend over time.