chemical-and-materials-engineering

2022-09-27

Best Practices for Matlab Code Sharing and Collaboration in Engineering Teams

project-root/
  src/          (main MATLAB functions and scripts)
  lib/          (third-party or shared utilities)
  data/         (input or sample data files, often read-only)
  output/       (generated results, logs)
  docs/         (documentation, README, design docs)
  tests/        (unit and integration tests)
  resources/    (non-code assets like images, models)

Use consistent naming conventions throughout the project. For example, prefix function names with a project abbreviation (mypkg_utils_parseInputs) to avoid collisions and make them instantly recognizable. Stick to a single case style (camelCase or snake_case) across the team. File names should describe purpose without relying on folder context — a file named computeStats.m is much clearer than script1.m.

Adopt MATLAB's built-in Project tool (available in the App Designer or via matlab.project) to define project paths, manage dependencies, and execute startup/shutdown scripts. A project file (.prj) ensures every team member loads exactly the same environment, reducing “works on my machine” issues.

Use Version Control Systems (Git)

Version control is non-negotiable for collaborative code. Git dominates the industry and pairs well with MATLAB. Host your repositories on a platform such as GitHub, GitLab, or Bitbucket. Establish a branching strategy that fits your team’s workflow — popular choices include feature branching and GitFlow.

Branching and Merging

• Keep the main (or master) branch always deployable. Every commit merged here should pass tests.
• Create short-lived feature branches off develop (if using GitFlow) or directly off main for simpler workflows. Name branches descriptively: feature/add-data-validation, bugfix/fix-off-by-one-error.
• Merge via pull requests (PRs) with a required code review. Squash-merge to keep history clean.
• Use rebase interactively before opening a PR to clean up commit history, but avoid rebasing shared branches.

Commit Message Conventions

Write commit messages that answer “why” and “what” — not just “how”. A good template:

Example:

feat(import): add support for CSV files with custom delimiters

Implement a new function `parseDelimitedFile` that accepts a delimiter
character. Update the existing `importData` wrapper to use it when
the file extension is '.csv'.

Closes #47


Encourage frequent, atomic commits — a single logical change per commit. Avoid giant commits that mix refactoring, bug fixes, and new features.


MATLAB-Specific Git Configuration

Add a .gitignore file that excludes generated files, such as *.asv (AutoSave), *.m~ (backup), slprj/ (Simulink generated), *.mat (large binary data), and the info.xml if not needed. Include only source code, documentation, and small configuration files in version control. For more guidance, see MathWorks documentation on tracking changes.

Write Modular and Reusable Code

Modular code is easier to understand, test, and reuse. Follow these principles:


  
One function, one responsibility. If a function does more than one distinct task, split it.
  
Keep functions short. A function that fits on one screen is easier to grasp. Long functions likely mix concerns.
  
Avoid global variables and global statements. Pass data explicitly as parameters. Use persistent variables sparingly and document their side effects.
  
Use MATLAB classes (value or handle) when encapsulating state and behavior together. Classes also simplify unit testing via dependency injection.
  
Write functions that return outputs rather than printing to the command window or writing to files. This makes them testable and composable.
  
Design for extensibility. Accept optional name-value pairs using inputParser or the newer arguments block (R2019b+).


For example, instead of a hard-coded script that reads a file, processes it, and plots, write a function that takes file path as input and returns processed data. Then a separate plotting function consumes that data. The same processing function can be reused in a batch pipeline or a GUI later.

Document Your Code Effectively

Documentation serves both current teammates and your future self. MATLAB supports two primary documentation paradigms.

In-Code Comments

Every function should begin with a help block (the first comment after the function signature). Include:


  
One-line description of the function’s purpose.
  
Detailed description, if needed.
  
Syntax examples showing typical usage.
  
Input arguments (name, type, default, description).
  
Output arguments (name, type, description).
  
See also references to related functions.


Use the doc command to test that your help block renders correctly. For example:

function out = computeMovingAverage(data, windowSize)
% computeMovingAverage  Smooth data using a moving average filter.
%
% Syntax:
%   y = computeMovingAverage(data, windowSize)
%
% Inputs:
%   data       - N-by-1 numeric vector
%   windowSize - positive scalar integer (number of points to average)
%
% Output:
%   y          - N-by-1 numeric vector, moving average result
%
% Example:
%   y = computeMovingAverage(randn(100,1), 5);
%
% See also: smoothdata, movmean


Add inline comments sparingly — explain the “why,” not the “what.” Clear code already shows the “what.”

External Documentation

Maintain a README.md in the repository root that describes the project, its dependencies, quick-start instructions, and how to run tests. For larger projects, use a wiki or a dedicated documentation site. MATLAB can publish Live Scripts (.mlx) with embedded outputs and formatted text — these make excellent tutorials or design documents. Keep them in the docs/ folder and version them.


Establish Coding Standards

Consistent style reduces cognitive load. Agree on a team-wide standard and enforce it automatically. Common items to standardize:


  
Indentation: Use 4 spaces per level (MATLAB’s default). Never mix tabs and spaces.
  
Variable naming: CamelCase (inputData) or snake_case (input_data) — pick one and stay consistent. Use descriptive names; avoid single-letter variables except for loop indices or common mathematical symbols.
  
Function naming: Lowercase start for functions, uppercase for classes (if using object-oriented). Use verbs for actions: parseFile, not fileParser.
  
Line length: Keep lines under 80–120 characters. Use ellipsis (...) for continuation.
  
Documentation: Mandate a help block for every public function.


Use MATLAB’s built-in Code Analyzer (the red/orange/green indicator in the editor) to catch common issues. Run checkcode('myfile.m') from the command line. For more rigorous checks, consider third-party tools like misshit or CheckMate.

Encourage Regular Code Reviews

Code reviews catch bugs early, spread domain knowledge, and improve overall design. Make them part of the pull request workflow.


  
Keep PRs small. A review should take no more than 30 minutes. If a PR is huge, break it into logical chunks.
  
Provide context. In the PR description, explain what changed and why, and any testing performed.
  
Review with a checklist. Does the code adhere to team standards? Are edge cases handled? Are there unit tests for new logic? Is the documentation updated?
  
Be constructive. Focus on the code, not the person. Offer suggestions, not commands.
  
Use comments to ask questions (“What happens when the input is empty?”) rather than just stating flaws.


For remote teams, schedule synchronous review sessions for complex changes. Otherwise, async reviews via GitHub/GitLab comments work well.

Leverage Collaboration Tools

Beyond version control, several tools can enhance real-time or asynchronous collaboration on MATLAB code.

MATLAB Drive and (MATLAB) Online

MATLAB Drive provides cloud storage that syncs across devices and lets team members share folders with controlled permissions. Use it for non-sensitive data, intermediate results, or shared reference scripts. MATLAB Online allows editing and running code in a browser — useful for quick demonstrations or onboarding new members without local setup.

Simulink and Model-Based Design

If your team uses Simulink, treat models as code. Use the same version‑control practices, and leverage Simulink Projects to manage model dependencies, data dictionaries, and version labels. Enable model comparison tools (visdiff) to review changes graphically.

Integrated Development Environments

Many teams edit MATLAB files in VS Code or IntelliJ with MATLAB extensions. This can provide better Git integration, linting, and code navigation. The key is that every developer uses the same “run configuration” — the same project file, path settings, and startup scripts.


For more on MATLAB’s collaboration features, see MATLAB Online product page.

Maintain Data and Code Security

Engineering teams often handle proprietary algorithms, customer data, or export‑controlled information. Protect these assets from the outset.


  
Use private repositories for sensitive code. GitHub, GitLab, and Bitbucket all offer private repos on free tiers for small teams.
  
Apply branch protection rules: require pull request reviews, status checks (e.g., CI passing), and prevent direct pushes to main.
  
Encrypt large files before storing them in version control. Use Git LFS with encryption or store data outside the repo and manage access separately.
  
Define access levels: not everyone needs write access. Use read-only tokens for CI/CD or deployment.
  
Set up regular backups of the repository and any associated data stores. Cloud-hosted solutions generally handle this automatically.
  
Be mindful of licensing. If you use open-source toolboxes or contributions from MathWorks File Exchange, understand their license terms. Don't accidentally include code with restrictive licenses in proprietary products.


Testing and Continuous Integration

Automated testing gives your team confidence to refactor and add features without breaking existing behavior. MATLAB provides the Unit Test Framework (since R2013a) which supports test suites, parameterized tests, and fixture setup/teardown.

Writing Unit Tests

Place each test file in the tests/ folder with a name like test_myFunction.m. Use a subclass of matlab.unittest.TestCase. Example:

classdef test_computeMovingAverage < matlab.unittest.TestCase
    methods (Test)
        function basicSmoothesCorrectly(testCase)
            data = [1 2 3 4 5];
            windowSize = 3;
            expected = [NaN 2 3 4 NaN];
            actual = computeMovingAverage(data, windowSize);
            testCase.verifyEqual(actual, expected, 'AbsTol', 1e-10);
        end
        
        function handlesEmptyInput(testCase)
            data = [];
            windowSize = 3;
            actual = computeMovingAverage(data, windowSize);
            testCase.verifyEmpty(actual);
        end
        
        function rejectsNonNumericInput(testCase)
            testCase.verifyError(@() computeMovingAverage('abc', 3), ...
                'MATLAB:invalidType');
        end
    end
end


Run all tests with runtests from the command line or set up a test runner that produces JUnit XML output for CI integration.

Continuous Integration

Use a CI service (GitHub Actions, GitLab CI, Jenkins, etc.) to run tests automatically on each push and pull request. For MATLAB, you can use the Run MATLAB Command action on GitHub or a Docker container with MATLAB installed. A typical CI pipeline:


  
Checkout the repository.
  
Install MATLAB (via license or container).
  
Run tests using runtests with code coverage reporting.
  
Check code quality with checkcode or a linter.
  
If all checks pass, merge or deploy.


Including CI ensures that no broken code lands on the main branch. See the MATLAB GitHub Actions documentation for setup instructions.

Dependency Management

MATLAB code often depends on specific toolboxes, custom libraries, or external data. Document these dependencies so every team member can reproduce the environment.



  
Use a requirements.txt file (or a setup.m script) that lists required toolboxes and their versions.
  
If you use MATLAB’s Add-On Explorer, commit the .mlappinstall or .mltbx files? Instead, document the Add-On URLs and versions in a project README.
  
For internal shared libraries, version them as submodules or as separate packages with a release tag.
  
Use MATLAB Project Dependencies (the project object) to automatically resolve paths and check for missing toolboxes.


Containerization

For reproducibility across operating systems and team members, consider packaging MATLAB code in a Docker container. MathWorks provides offical Docker images (with license required) that include MATLAB Runtime or full MATLAB. Combine with a Dockerfile that installs additional toolboxes and sets up your project. This is especially valuable when deploying models to production or sharing code with external collaborators who lack a full MATLAB license.

A minimal Dockerfile might look like:

FROM mathworks/matlab:r2023b
COPY . /workspace
WORKDIR /workspace
CMD ["matlab", "-batch", "runtests"]


For more, see MathWorks guidance on MATLAB in Docker.

Training and Onboarding

Even the best practices are useless if the team doesn’t adopt them. Invest in onboarding materials and continuous learning.


  
Create a New Starter Checklist that covers setting up version control, cloning the repo, installing toolboxes, running tests, and understanding the branch workflow.
  
Hold a short workshop on Git basics (or MATLAB‑specific Git workflows) when new members join.
  
Pair senior and junior developers on code reviews and pairing sessions to transfer knowledge.
  
Maintain a team wiki or internal blog with common recipes, troubleshooting tips, and design decisions.


Metrics and Continuous Improvement

Track how your team is doing with code sharing and collaboration. Useful metrics include:


  
Code review turnaround time — median time from PR open to merge.
  
Test coverage — increase over time.
  
Number of commits per week — indicates activity, but not quality.
  
Build stability — percentage of CI runs that pass on main.


Review these metrics quarterly in a post‑mortem or retrospective. Celebrate improvements and identify bottlenecks. Maybe the team needs to adjust the branching strategy, add more tests, or invest in better documentation.

Conclusion

Sharing and collaborating on MATLAB code effectively requires deliberate structure, robust tooling, and a team culture that values quality. By organizing projects clearly, using version control with disciplined workflows, writing modular and documented code, enforcing standards, reviewing thoroughly, and automating testing, engineering teams can eliminate friction and focus on solving real engineering problems. Security, dependency management, and continuous learning complete the picture. Start by picking two or three practices from this guide and implement them over the next sprint — you’ll see immediate improvements in both productivity and code health.