Error seed can be used to estimate the number of residual errors in a system. Error seeding, as the name implies, seeds the code with some known errors. In other words, some artificial errors are introduced into the program artificially. The number of these seeded errors detected in the course of the standard testing procedure is determined.
These values in conjunction with the number of unseeded errors detected can be used to predict:
• The number of errors remaining in the product.
• The effectiveness of the testing strategy. Let N be the total number of defects in the system and let n of these defects be found by testing. Let S be the total number of seeded defects, and let s of these defects be found during testing.
n/N = s/S or N = S × n/s
Defects still remaining after testing = N–n = n×(S – s)/s
Error seeding works satisfactorily only if the kind of seeded errors matches closely with the kind of defects that actually exist. However, it is difficult to predict the types of errors that exist in a software. To some extent, the different categories of errors that remain can be estimated to a first approximation by analyzing historical data of similar projects. Due to the shortcoming that the types of seeded errors should match closely with the types of errors actually existing in the code, error seeding is useful only to a moderate extent.