One of the most powerful features of SDM is realized when multiple influence relationships are connected forming a circular relationship known as a feedback loop a concept that reveals that any actor in a system will eventually be affected by its own action. For example, the test cases execution activity from Figure 2 would be represented with metrics corresponding to execution time and effectiveness in terms of identifying failures. To examine the effect of defect avoidance practices on reliability, we integrated SDM with existing reliability prediction and growth models. In a continuous simulation, time increases with constant increments. InitialFailureRate is the failure rate at the beginning of system testing.
Failures is the number of failures encountered from the start of system testing to time t.
TotalFailures is the total number of failures expected. The model s users should define these two functions according to historical data collected for their own projects. Here C is a constant called transformation ratio, that depends on the application type, and was empirically determined as presented in Table 1. Simulator users can replace the formula and table above with any other model that best describes their project. Our current model follows a generic waterfall life cycle and is populated with default metrics based on industry averages.