Analysis of a Simple Debugging Model.

Abstract

A system has an unknown number of faults. Each fault causes a failure of the system, and is then located and removed. The failure times are independent exponential random variables with common mean. A Bayesian analysis of this model is presented, with emphasis on the situation where vague prior knowledge is represented by limiting, improper, prior forms. This provides a test for reliability growth estimates of the number of faults, an evaluation of current system reliability, and a prediction of the time to full debugging. Three examples are given. Keywords: Bayes factor; Improper prior; Non-homogeneous Poisson process; Reliability growth; Software reliability.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 30, 1986
Accession Number
ADA176222

Entities

People

  • Adrian Raftery

Organizations

  • University of Washington

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Computer Programs
  • Data Science
  • Data Sets
  • Debugging
  • Engineering
  • Estimators
  • Information Science
  • Military Research
  • Probability
  • Random Variables
  • Reliability
  • Software Development
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Universities

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Science.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference