Summary of Technical Progress on Bayesian Software Prediction Models.
Abstract
Work has been completed on development of Bayesian Software Correction Limit Policies designed to determine the optimum time value that minimizes the long run average cost of debugging at two levels - correction action undertaken by the programmer (Phase I) and action undertaken by a system analyst or system designer, if the error is not corrected in Phase I (Phase II). Two models are developed - one assuming the cost of observations of error occurrence and correction time, prior to implementation of the optimum policy, is negligible; the other incorporating the cost of observations. Work was also completed on an Imperfect Software Debugging Model that assumes errors are not corrected with certainty. By assuming the initial number of errors, probability of successfully correcting an error, and constant error occurence rate are all known, formulas for such quantities as distribution of time to completely debugged software, distribution of time to a specified number of remaining errors, and expected number of errors detected by time t can be derived. Work is currently in progress in extending the Imperfect Debugging Model to incorporate error correction time, estimation of model parameters and development of a Bayesian model; developing bivariate software reliability models where system errors are classified as serious and non-serious; development of empirical models for software error data; development of software reliability demonstration plans for making accept/reject decisions for software packages; and investigating the effects of changes in prior distributions and/or model parameters on quantities of interest. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1977
- Accession Number
- ADA039022
Entities
People
- Amrit L. Goel
Organizations
- Syracuse University