Seeding/Tagging Estimation of Software Errors: Models and Estimates.
Abstract
This report concerns itself with seeding/tagging estimates of the number of software errors based on the number of errors either inserted deliberately in a program (seeded) or found by debugging (tagged), the number of errors found by a debugger unaware of the first set, and the number of errors appearing in both sets. Estimates from 3 models are discussed. Model 1 assumes all errors are equally open to discovery at all times. Model 2 and 3 assume categories of difficulty exist and that any error which appears can be assigned to the proper category. Model 2 does not assume the relative distribution of errors among categories is known, while Model 3 does. The mean and mean-squared error of a maximum likelihood estimate and a modified maximum likelihood estimate are given for all 3 models. It is shown how these quantities vary with certain relations among the total number of errors, size of tagged or seeded set, and size of accompanying sample set. A procedure for determining optimum values for size of tagged or seeded set and number found by the second debugger is outlined. Finally, multi-trial estimates for parameters are found and compared with single-trial estimates. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1977
- Accession Number
- ADA036655
Entities
People
- B. Rudner