4. Quick Start Guide¶
This chapter will give you a quick overview of the functionality of MQC and will show you how to use it to analyze your project quality and data.
4.1. Open Sample Project¶
Open a browser, navigate to the MQC server (e.g. http://mes-qualitycommander.com) and login with your personal user credentials.
There are two ways to open the sample project.
Either click on the entry
Open SDOAcar (Demo project) within the
section inside the main window of the tool or
choose to open the dialog on the left hand side
(click on the
+ in the top left corner) and there open the sample project
by clicking on
Open SDOAcar within the section.
4.2. Check Data Availability¶
MQC structures and shows information on specific pages (see Pages and Visualizations in Detail). After loading the SDOACar sample project, the Dashboard page is shown, which is the default when creating a new project. Switch between pages via selecting the corresponding tab on top of the main visualization area.
This introduction chapter starts with the Data Availability to explore the features and functionalities of MQC. So, please, select the Data Availability page to continue.
The Data Availability page gives you a first overview of the imported data. This page helps you to evaluate, which of the data you expect to be collected is actually available or missing. Availability is shown per artifact, per data source and for the whole project (see Figure 4.2).
Data availability can be checked per revision by scrolling from the last to the first revision in the Availability Matrix. Additionally, the Availability Distribution bin chart at the top of the page depicts for each revision the relative portions of available and missing data.
MQC uses a blue data coloring scheme on pages related to data. In context of the Data Availability page, this means you can easily distinguish between available data in blue and missing data in grey. Lightblue indicates propagated data, which means data that is missing for a particular revision but was imported for any of the previous revisions (refer to Data Propagation for more details).
White areas inside the Availability Matrix indicate data excluded from the project (see Context Categories).
4.3. Check Quality Status¶
The Quality Status page provides detailed information on the quality that is computed from the available data. Here you can check quality for each of the artifacts separately resp. for each quality property per artifact at a certain point in time.
A Quality Bin Distribution chart depicts for each revision the relative portions of quality properties belonging to a certain quality category. Per default, MQC uses categories of good, acceptable and bad quality.
Initially, in MQC Quality is visualized using a traffic light coloring scheme, i.e. green for good quality and red if the computed quality is bad. Therefore, you can easily detect problems, i.e. those parts of your project with insufficient quality.
MQC allows to customize the used bin categories, see Quality Model and Quality Bin Configuration. This also includes the color scheme.
The quality matrix shown in the main visualization window of this page can be used to gain information about a particular quality property for a certain artifact.
Grey areas inside the Quality Matrix indicate missing quality properties, which means the data used to calculate the quality property is not available for that particular revision. Besides, white areas are shown for quality that is based on data excluded from your project (see Context Categories).
For details regarding quality calculation, please check the MQC Quality Concept.
4.4. Work with MQC¶
As you may have noticed, all pages use a common layout. A Project KPI on the top-left always informs you about the overall quality respectively data availability of your project including a trend visualizing the progress over the past revisions. On the right-hand side of the Project KPI, you find a revision based bin chart, where each bin shows relative portions of certain sets of elements compared to other sets, i.e. available data vs. missing data or good quality items vs. bad quality items.
On the left-hand side a list of selectors is available, which enables you to reduce the displayed information, e.g. by selecting one or multiple artifacts, quality properties or data sources.
Each page contains a main visualization area, where you can find all necessary information regarding project quality resp. data availability, and which always reflects your selections previously done.
You can now start identifying the reason for bad quality. The general concept of detecting issues is to click on (or hover over) e.g. red quality bins or tiles. At the Quality Status page select the orange tile in the bottom-left of the Quality Matrix.
By hovering over the tile, you get detailed information about the particular quality property, for example the specific quality measure value.
Then click on the icon next to the Figure 4.5.entry at the left-hand side panel as shown in
If the menu panel on the left-hand side is not enabled, click on the
Menu icon within the MES Quality Commander banner in the top-left (see
Figure 4.5). Per default the menu
is always enabled on all MQC pages.
MQC directly switches to the Data Status page and reduces the visualization to those base and derived measures that were used to calculate the selected quality properties within the chosen revision.
Please note, this selection is kept when you now navigate between the data pages to get all necessary information related to the selected data.
For more information on the MQC Data Details functionality, please refer to Data Details Drill Down.
To reset the data details selection, you should click on the icon next to themenu entry at the left-hand side panel.
Reset all other markings by simply clicking on the Project KPI tile.