The concept of coupling was defined in the 1980 book “The Practical Guide to Structured Systems Design” by Meiler Page-Jones to be “the degree of dependence of one module upon another; specifically, a measure of the chance that a defect in one module will appear as a defect in the other, or the chance that a change in one module will necessitate a change to another.”

He identified two fundamental flavours of coupling and defined them as:

control coupling—a type of coupling in which one module communicates information to another module for the explicit purpose of influencing the latter, and
data coupling—a form of coupling in which one module communicates information to another

Let’s look at an example to clarify the differences between the two.

An aircraft engine control system consumes air data information such as altitude and airspeed, but there are different control algorithms used when the aircraft is on the ground versus in the air. The choice of which algorithm to use is determined by a “weight on wheels” signal from the landing gear systems that indicates whether the aircraft is on the ground or not. In this example, the dependence of the engine control system on the altitude and airspeed data streams indicates a strong data coupling with the systems that provide them. Similarly, the “weight on wheels” signal indicates strong control coupling between the engine control systems and the landing gear systems.

Coupling metrics can be used to improve software quality throughout the software design and verification process. The intent is to show that the software modules affect one another only in the ways intended by the software design, ensuring that there are no unplanned, anomalous, or erroneous behaviours. Documenting data and control coupling during design provides a set of requirements to test during the software integration process. Similarly, ensuring that the data and control coupling between modules are exercised and exhibit no faults during software test demonstrates that the integration and architecture of the software is fully verified.

DO-178C requires that a control and data coupling assessment be performed on safety-critical software to ensure that these design, integration, and test objectives are met. Measuring control and data coupling is achieved via a combination of control and data flow analysis.

Control flow analysis is performed both on a programs calling hierarchy and on individual procedures.

Data Flow Analysis follows variables through the source code and reports any anomalous use. This check is performed both at the procedure level and as part of system-wide analysis. It is a very powerful technique that, not only provides the basis for a data coupling assessment, but also detects other serious problems, such as variables that are used before they are initialized or an array that is accessed outside of its bounds.

Although it is easy to assume from the naming that control flow analysis provides visibility of control coupling and data flow analysis of data coupling, this is not the case. Both control flow and data flow analyses are necessary for assessing both control and data coupling.

Irrespective of the demands of DO-178C, measuring the cohesion of software modules via control and data coupling helps to yield improvements in software testability and maintainability while reducing the impact of change, simplifying software reuse.

Coupling

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