Glossary

Artifact

Artifacts are any objects for which quantitative data will be collected in your project. They are therefore the lowest level of objects for which, based on the collected data, a quality assessment is performed in MQC. Examples for Artifacts are Simulink-Models, submodules and components or the documentation.

Adapter

Adapters are used to import data from external sources into MQC. There is an Adapter for each tool that is supported by MQC.

Adapter Configuration

In order to import data using an Adapter, you have to first configure the Adapter providing the required parameters (e.g. the path to the file containing the data

Alias

In MQC, objects like Artifacts and Data Sources are identified by name. An Alias is an alternative identifier for an object.

Bundles

Use Bundles in order to define certain structure with respect to your Work Products.

Bundles can be nested arbitrarily.

Bundles are used to group Artifacts and other Bundles.

Batch (Job, Automation, Mode, …)

You are able to run MQC in Batch Mode

Configuration

Here the user can manage everything regarding Data.

Data Import, Export, Input or Adapters can be maintained using MQC Configuration Panel.

Context Category

Via Context Categories you can define an association between Data Sources and Artifacts. Particularly, by your Context Category’s definition expected data can be computed.

Dashboard

The pages visible within a MQC project containing one or more visualizations.

Data Source Group / Data Source

Data Sources are objects providing so-called raw data as a quality computation’s foundation.

By Data Source Groups a grouping option for Data Sources is given.

Data Source Value / Data Source Value Names / Data Entry

Each Data Source yields Values (in general, these values are real numbers).

Names can be “words” like “passed”, “GlobalComplexity_Ref1”, “aborted”, …

A Data Entry consists of a Name and a Data Source Value List.

Drilldown

Using MQC/Spotfire features the user is able to discover imported data right up to original tool reports (Drilldown).

Export

One can export the project’s structure (including or excluding data) into an XML file.

Visualizations and Pages can be exported, too (export is with respect to PDF, Power Point, HTML, …)

Filter Panel

MQC Filter Panel supports the user’s Drilldown and data discovery.

All the visualizations react/get adapted when Filtering is active.

Filtering

Filtering means that displayed information is hidden (by user’s Filtering).

Import Data

The process of making data available in MQC is summarized by the term Import Data.

Import Project Structure

MQC is able to import the project structure by using its tool Adapters (You can check the Adapter Configuration in order to see what tool’s XML is supported).

Mapping Metric

This is a Metric where Data Values are mapped (=assigned to real numbers between 0 and 1) before quality value computation will start. This is useful when your Data Source has Data Source Values which represent counts of different result types (for example no of failures and successes).

Mapping

A Mapping is a kind of function and assignment, respectively.

Manual Data Input

The user is able to type in data manually, if needed (we do not recommend this anyway).

Marking

The user can perform left clicks on (almost) everything which is visible in MQC (mainly with respect to visualizations).

Marking can be “page comprehensive” (this fact is the Selection Details Page’s foundation).

In general, Marking is slightly related to Filtering.

Metric

A Metric is a function which computes a (quality) value between 0 and 1 out of “raw data” (which come from Data Sources).

Project Configuration

Here, everything regarding your project (Metrics, Artifacts, Data Sources, Revisions …) can be defined.

Quality

Easy general definition is not available, yet.

Quality Model

Quality Models provide grouping options for Quality Aspects providing grouping options for Metrics.

Quality value computation can be assigned to certain Revisions by Quality Models.

At least one Quality Model will be needed if MQC shall compute quality and provide visualizations.

Quality Aspect

See Quality Models.

Quick Data Import

You are able to import data (with respect to one Revision) fast via Quick Data Import.

Quality Metric Pool

Here, Metrics can be defined end edited.

Revision

Use Revisions to map your project’s “time structure” into MQC.

Report (Report file, Tool reports)

(XML, PDF, HTML…) files from (quality) tools are called Reports.

Suppress Dashboard Update

You can suppress Visualization’s refresh in order to edit Project Configuration

Latency is reduced extremely.

Standard Metric

This is a kind of Metric for which a Metric expression is given.

Metric expression is a mathematical term.

Visualization

A visualization is an object where you can see relations with respect to imported data.

Work Products

Your project is the “first” Work Product.

Artifacts and Bundles are Work Products, too.

XML File

A file containing structured information following a XML Schema.

Another glossary

A
Assessment
Assessment catalog
Assessment work progress
B
Back-to-back test
Baseline
Batch mode	Special execution mode for applying a series of test activities (e.g. simulation, evaluation, documentation, etc.) on a given set of test sequences
C
Checked region
Complied vector
Condition coverage
D
Debug simulation
Decision coverage
Dynamic test
E
ECU	Electronic control unit
End-of-test
Equivalence class
Evaluation
H
Hardware-in-the Loop (HiL)	HiL simulation: technique that is used in the development and test of complex real-time embedded systems.
Host simulation	See software-in-the-loop
I
Initialization (MTCD)
Integration test
Interface
Interface analysis
M
Metric
Model test
Model-in-the-Loop	Simulation of an (embedded) system in an early phase of software development where usually the system is represented using floating-point arithmetic
Module test
P
Parameter script
Processor-in-the-Loop	Simulation of generated software (represented in fixed-point arithmetic) on the target hardware
R
Reference data
Regression test	Test of a previously tested software after maintenance and/or modification in order to assure that defects have not been introduced into the software.
S
Signal tolerance
Signal comparison catalog
Signal function
Simulation mode
Smoke test
Software-in-the Loop	Simulation of the generated software (represented in fixed-point arithmetic) on the development hardware
State machine	A computational model based on a finite number of states, transitions, and actions
Statement coverage
Step size	Time interval used for sampling the simulation data
T
Target simulation	See processor-in-the-loop
Test case
Test catalog
Test object
Test oracle
Test setup
Test step
Test bed
Test data
Test group	This information helps
Test harness	This is an explanation
Test protocol	See test sequence report
Test sequence	This is a good explanation
Test sequence report
Test sequence work progress
Testware
Traceability
Trigger
Trigger performance
U
Utility function
V
Validation	Examination whether the software fulfills the given requirements with regard to the intended use
Verification	Checking whether the software complies to the specification given by the requirements

References

Corr, Lawrence / Stagnitto, Jim (2014): Agile Data Warehouse Design. Collaborative Dimensional Modeling from Whiteboard to Star Schema. DecisionOne Press.