Quantitative process control is supported by statistical process control techniques and methodologies. Statistical process control (SPC) is a discipline based on measurements and statistics [5–8]. Decisions are made and plans developed based on the actual collection and evaluation of measurement data rather than on intuition and past experience [5]. The basis for SPC is a view of the development (or testing) process as a series of steps, each of which is a process in itself with a set of inputs and outputs. This view is shown in Figure 15.2. Ideally the output of each step is determined by rules/procedures/standards that prescribe how it is to be executed. Practically speaking the outcome of a step may be different then expected. The differences are caused by variations. Variations may be due to, for example, human error, influences outside of the process, and/or unpredictable events such as hardware/software malfunctions. If there are many unforeseen variations impacting on the process steps, then the process will be unstable, unpredictable, and out of control. When a process is unpredictable then we cannot rely upon it to give us quality results.