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Collect Statement and Function Coverage Metrics for MATLAB Source Code

When you run tests, you can collect and access code coverage information for your MATLAB® source code by adding an instance of the matlab.unittest.plugins.CodeCoveragePlugin class to the test runner. In MATLAB, a plugin created using a matlab.unittest.plugins.codecoverage.CoverageResult or matlab.unittest.plugins.codecoverage.CoverageReport format object provides information about statement and function coverage. For more information about statement and function coverage, see Types of Code Coverage for MATLAB Source Code.

This example shows how to collect, programmatically access, and generate reports including statement and function coverage information for source code in a file. The file defines the QuadraticPolynomial class, which represents quadratic polynomials. The class constructor first validates that the coefficients of the polynomial are numeric values and then uses these values to initialize the class properties. The class includes the solve method to return the roots of the specified quadratic polynomial, and the plot method to plot the polynomial around its axis of symmetry. To view the complete code for QuadraticPolynomial, see QuadraticPolynomial Class Definition.

Run Tests and Analyze Code Coverage Information

In your current folder, save the QuadraticPolynomial class definition in a file named QuadraticPolynomial.m. Then, create the QuadraticPolynomialTest1 test class in your current folder. Add two Test methods to the class that test the solve method against real and imaginary solutions.

classdef QuadraticPolynomialTest1 < matlab.unittest.TestCase
    methods (Test)
        function realSolution(testCase)
            p = QuadraticPolynomial(1,-3,2);
            actSolution = p.solve();
            expSolution = [1 2];
        function imaginarySolution(testCase)
            p = QuadraticPolynomial(1,2,10);
            actSolution = p.solve();
            expSolution = [-1-3i -1+3i];

To run tests and perform a code coverage analysis, first create a test runner with a plugin that provides programmatic access to the statement and function coverage information for source code in the file QuadraticPolynomial.m.

import matlab.unittest.plugins.CodeCoveragePlugin
import matlab.unittest.plugins.codecoverage.CoverageResult

runner = testrunner("textoutput");
format = CoverageResult;
p = CodeCoveragePlugin.forFile("QuadraticPolynomial.m",Producing=format);

Create a test suite from the QuadraticPolynomialTest1 class and run the tests. The tests pass.

suite1 = testsuite("QuadraticPolynomialTest1");
Running QuadraticPolynomialTest1
Done QuadraticPolynomialTest1

After the test run, the Result property of the CoverageResult object holds the coverage result. Access the statement coverage summary from the coverage result. The returned vector indicates that the tests executed 8 out of the 17 statements in the file, resulting in 47% statement coverage. The statement coverage is low because the tests did not execute the code that throws an error and the code within the plot method.

result1 = format.Result;
statementSummary = coverageSummary(result1,"statement")
statementSummary = 1×2

     8    17

Access the function coverage summary. The summary indicates that the function coverage is 75% because the tests missed one of the four methods in the QuadraticPolynomial class (plot).

functionSummary = coverageSummary(result1,"function")
functionSummary = 1×2

     3     4

Now, generate an HTML code coverage report from the coverage result. The report displays information about statement and function coverage and uses different colors to highlight the executed or missed statements and functions.


You can interact with the HTML code coverage report. For example, in the Overall Coverage Summary section, you can select a coverage type from the Currently viewing list to view detailed information about that coverage type. This figure shows the Source Details section for statement coverage, which displays all the statements that were covered or missed.

Run Additional Tests to Improve Coverage

Create tests to execute the parts of the source code that were not executed by suite1. In your current folder, create another test class named QuadraticPolynomialTest2. Add a Test method to the class that tests against nonnumeric inputs and another Test method that tests properties of a plotted polynomial.

classdef QuadraticPolynomialTest2 < matlab.unittest.TestCase
    methods (Test)
        function nonnumericInput(testCase)
            testCase.verifyError(@()QuadraticPolynomial(1,"-3",2), ...
        function plotPolynomial(testCase)
            p = QuadraticPolynomial(1,-3,2);
            fig = figure;
            ax = axes(fig);
            actYLabelText = ax.YLabel.String;
            expYLabelText = '1.00x^2-3.00x+2.00';

Create a test suite from the QuadraticPolynomialTest2 class and run the tests. The tests pass.

suite2 = testsuite("QuadraticPolynomialTest2");
Running QuadraticPolynomialTest2
Done QuadraticPolynomialTest2

In this example, you ran different test suites to qualify the same source code. To report on the total aggregated coverage for these test runs, generate an HTML report from the union of the coverage results corresponding to the test runs. The report shows that the two test suites combined fully exercised the statements and functions in the source code.

result2 = format.Result;
generateHTMLReport(result1 + result2)

QuadraticPolynomial Class Definition

This code provides the complete contents of the QuadraticPolynomial class.

classdef QuadraticPolynomial
        A,B,C   % Coefficients of a*x^2 + b*x + c

        function obj = QuadraticPolynomial(a,b,c)
            if ~isa(a,"numeric") || ~isa(b,"numeric") || ~isa(c,"numeric")
                error("QuadraticPolynomial:InputMustBeNumeric", ...
                    "Coefficients must be numeric.")
                obj.A = a; obj.B = b; obj.C = c;

        function roots = solve(obj)
            % Return solutions to a*x^2 + b*x + c = 0
            delta = calculateDelta(obj);
            roots(1) = (-obj.B - sqrt(delta)) / (2*obj.A);
            roots(2) = (-obj.B + sqrt(delta)) / (2*obj.A);

        function plot(obj,ax)
            % Plot a*x^2 + b*x + c around its axis of symmetry
            delta = calculateDelta(obj);
            x0 = -obj.B/(2*obj.A);
            x1 = abs(sqrt(delta))/obj.A;
            x = x0 + linspace(-x1,x1);
            y = obj.A*x.^2 + obj.B*x + obj.C;

    methods (Access=private)
        function delta = calculateDelta(obj)
            delta = obj.B^2 - 4*obj.A*obj.C;

See Also


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