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name: matlab-modernize-code description: > Modernize deprecated MATLAB functions and patterns. Use when check_matlab_code or checkcode reports "not recommended" or "to be removed" warnings, when migrating legacy code, or when replacing deprecated APIs (trainNetwork, csvread, xlsread, datenum, eval, subplot, guide, optimset, wavread, svmtrain, uicontrol) with current equivalents. license: MathWorks BSD-3-Clause metadata: author: MathWorks version: "1.2"

Code Modernization

Replace deprecated MATLAB functions and anti-patterns with modern equivalents. This skill is the resolver — check_matlab_code is the detector.

When to Use

• check_matlab_code or checkcode returns "not recommended" or "to be removed" warnings
• User asks to modernize, migrate, or update old MATLAB code
• Code uses functions listed in the quick reference table below
• After static analysis reveals deprecated API usage
• Writing new code in a domain that has known deprecated patterns

When NOT to Use

• Reviewing code quality broadly — use matlab-review-code (which may then trigger this skill)
• Debugging runtime errors — use matlab-debugging
• Performance profiling — use performance skills (though anti-patterns below overlap)

Quick Reference: Top Deprecated Functions

Deprecated	Use instead	Since	Category
csvread / dlmread	readmatrix	R2019a	File I/O
csvwrite / dlmwrite	writematrix	R2019a	File I/O
xlsread	readtable, readmatrix	R2019a	File I/O
xlswrite	writetable, writematrix	R2019a	File I/O
datenum / datestr	datetime	R2014b	Date/Time
subplot	tiledlayout / nexttile	R2019b	Graphics
eval / evalc / evalin	Dynamic field names, function handles	—	Security
str2num	str2double	—	Security
trainNetwork	trainnet	R2024a	Deep Learning
LayerGraph / SeriesNetwork	dlnetwork	R2024a	Deep Learning
classify (DL)	minibatchpredict + scores2label	R2024a	Deep Learning
uicontrol	uibutton, uidropdown, etc.	R2016a	UI/App
guide	appdesigner	R2025a	UI (Removed)
optimset	optimoptions	R2013a	Optimization
strmatch	startsWith, matches	R2019b	Strings
clear all	clearvars	—	Performance
webmap	geoaxes + geobasemap	R2025a	Mapping

Critical Anti-Patterns

Never use these in new code:

Anti-pattern	Problem	Use instead
eval / evalc / evalin	Security risk, prevents JIT optimization, difficult to debug	Dynamic field names s.(name), function handles
str2num	Uses eval internally — code injection risk	str2double
Growing arrays in loops	O(n²) memory reallocation	Preallocate with zeros, cell
global variables	Hidden state, performance penalty	Pass as arguments or use structs
clear all	Removes functions from memory, forces recompilation	clearvars
cd during execution	Forces function re-resolution	fullfile for paths
exist('var','var') in loops	Expensive state query	Initialize variable before loop
Large data in code	Slow parsing, hard to maintain	Save to .mat or .csv files

Modern Design Patterns

Prefer these in all new code:

Table-Based Workflows

data = readtable('sensors.csv');
data.Timestamp = datetime(data.Timestamp);
data.Status = categorical(data.Status);
recentData = data(data.Timestamp > datetime('today') - days(7), :);
summary = groupsummary(recentData, 'SensorID', 'mean', 'Value');

String Arrays (not char arrays)

name = "John";                        % not 'John'
names = ["John", "Jane", "Bob"];      % not {'John','Jane','Bob'}
fullName = firstName + " " + lastName; % not [first,' ',last]
idx = contains(names, "Jo");          % not cellfun + strfind

Arguments Block (not nargin/varargin)

function result = processData(data, options)
    arguments
        data (:,:) double
        options.Method (1,1) string {mustBeMember(options.Method, ["fast","accurate"])} = "fast"
        options.Verbose (1,1) logical = false
    end
end

Vectorization (not loops)

% Instead of: for i=1:n, V(i) = pi/12*(D(i)^2)*H(i); end
V = pi/12 * (D.^2) .* H;

% Instead of: loop with if
Vgood = V(D >= 0);   % logical indexing

Preallocation

result = zeros(1, n);     % numeric
C = cell(1, n);           % cell array
S(n) = struct('f1', []);  % struct array

Key Migrations

File I/O: csvread/xlsread → readmatrix/readtable

% Old                          → Modern
M = csvread('data.csv');       % M = readmatrix('data.csv');
M = dlmread('data.txt','\t'); % M = readmatrix('data.txt','Delimiter','\t');
[n,t,r] = xlsread('f.xlsx');  % T = readtable('f.xlsx');
csvwrite('out.csv', M);       % writematrix(M, 'out.csv');
xlswrite('out.xlsx', data);   % writetable(T, 'out.xlsx');

Deep Learning: trainNetwork → trainnet

% Old: classificationLayer specifies loss implicitly
net = trainNetwork(X, Y, layers, options);

% Modern: specify loss explicitly, no classificationLayer needed
net = trainnet(X, Y, layers, "crossentropy", options);

% Prediction
scores = minibatchpredict(net, XTest);
YPred = scores2label(scores, classNames);

eval → Dynamic Field Names / Function Handles

% Old: eval([varName ' = 42;']);
s.(varName) = 42;

% Old: result = eval(['process_' method '(x)']);
handlers.fast = @processFast;
handlers.slow = @processSlow;
result = handlers.(method)(x);

References

Load these when working in a specific domain:

Load when...	Reference
Deprecated core MATLAB functions (file I/O, strings, deep learning, UI)	reference/core-functions-guidance.md
Performance anti-patterns, vectorization, preallocation	reference/performance-guidance.md
Signal processing deprecated functions	reference/signal-processing-guidance.md
Audio/video I/O migration (wavread, aviread)	reference/audio-video-guidance.md
Optimization toolbox (optimset, optimtool)	reference/optimization-guidance.md
Control systems plot options	reference/control-systems-guidance.md
Image processing ROI objects	reference/image-processing-guidance.md
Statistics/ML (svmtrain, dataset, classregtree)	reference/statistics-ml-guidance.md
Simulink configuration and blocks	reference/simulink-guidance.md
Functions completely removed (guide, optimtool, fints, wavread)	reference/removed-functions-guidance.md
Communications System objects	reference/communications-guidance.md
Mapping Toolbox (webmap, wmmarker, wmline, geotiffread, mfwdtran, makerefmat)	reference/mapping-guidance.md

Conventions

• Always run check_matlab_code first — let static analysis find deprecated usage
• After checkcode, scan the source for patterns checkcode misses: subplot (not flagged), str2num (sometimes not flagged), global variables, growing arrays may only warn about size change
• Fix deprecated patterns before other code quality issues
• When writing new code, use the modern pattern from the start — don't write deprecated code and fix it later
• For functions marked "Removed" — they will cause immediate errors, not just warnings
• When migrating, test the modern replacement against the old behavior to confirm equivalence
• Consult the domain-specific reference file for detailed migration patterns with code examples

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