Open the Model and Enable Coverage Analysis

Load the model into memory.

modelName ='slvnvdemo_covfilt'; load_system(modelName);

Use aSimulink.SimulationInputobject to configure coverage for the model.

covSet = Simulink.SimulationInput(modelName); covSet = covSet.setModelParameter('CovEnable','on'); covSet = covSet.setModelParameter('CovMetricStructuralLevel','MCDC'); covSet = covSet.setModelParameter('CovSFcnEnable','on'); covSet = covSet.setModelParameter('StopTime','20'); covSet = covSet.setModelParameter('CovSaveSingleToWorkspaceVar','on'); covSet = covSet.setModelParameter('CovSaveName','covData');

Simulate the model using theSimulationInputobject as the input.

simOut = sim(covSet);

View the coverage results before applying a filter. You can access the coverage usingdecisioninfo, or you can view the HTML report usingcvhtml.

covInitial = decisioninfo(covData,[modelName,'/Saturation']); percentInitial = 100 * covInitial(1)/covInitial(2)

percentInitial = 50

cvhtml('covReportInitial',covData)

Bothdecisioninfoandcvhtml显示相同的结果覆盖50%的决定。如果you don't intend your current tests to exercise this outcome, you can justify the outcome so it is no longer reported as missing coverage.

In this example, we justify thefalsedecision outcome of theinput > lower limitdecision objective in the Saturation block.

Justify the Missing Condition Objective

MetricSelectorobjects accept the block path or the block handle as the second input. Get the block handle of the Saturation block by usinggetSimulinkBlockHandle.

id = getSimulinkBlockHandle([modelName,'/Saturation']);

Because the objective being justified is a decision outcome, the first input to the metric selector constructor isslcoverage.MetricSelectorType.DecisionOutcome. The second input is the block handle. The last two are the index of the objective to justify and the index of the outcome of that objective, respectively.

Because theinput > lower limitdecision objective is the first objective for the Saturation block, its objective index is1. Because thefalseoutcome of this objective is the first outcome, its outcome index is also1. Therefore, the last two inputs are1,1.

metr = slcoverage.MetricSelector(slcoverage.MetricSelectorType.DecisionOutcome,id,1,1);

Create a filter and rule. In this case, we use the default filter mode of justify. Then add the rule to the filter using theaddRulemethod.

filt = slcoverage.Filter; rule = slcoverage.FilterRule(metr,'Expected result'); filt.addRule(rule);

Save the filter to a filter file using thesavemethod. Then apply the filter file to thecvdataobject by assigning thefilterproperty to the new filter file.

filt.save('metrfilter'); covData.filter ='metrfilter';

Re-generate the coverage results for the Saturation block using the new filteredcvdataobject.

covFiltered = decisioninfo(covData,[modelName,'/Saturation']); percentInitial = 100 * covFiltered(1)/covFiltered(2)

percentInitial = 75

cvhtml('covReportFiltered',covData)

In the HTML report, the missing decision outcome is highlighted to indicate that it is justified. Decision coverage for the Saturation block is now 75%.