5.3. Data Propagation

Data imported into MQC is assigned to a revision depending on the point in time the data was collected, i.e. the report has been created (see Revisions).

Missing data in terms of not yet available respectively not yet imported into MQC, leads to missing quality, which has an impact on the overall quality of the project, because per default missing quality is treated as 0% (see Aggregation).

Therefore, data that was available in previous revisions can be used in later revisions as well until it is replaced with data from a newly created report, e.g. after a test re-execution.

Using data propagation, MQC is able to calculate and visualize certain quality metrics even for those revisions, where the data used to calculate the particular quality metric has not been re-collected respectively re-imported.

Data may become invalid, if for instance the artifact changes (new model version), but the data is not re-collected.

Therefore the user must decide carefully if and which data to propagate.

In any case, data is replaced if more up-to-date data - new data collected for the same object - is loaded into MQC.

Details about how to switch on or off data propagation in MQC can be found in Propagation of data.