Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) User's Guide. Revision 3

Abstract

This is the third in a series of Naval Surface Warfare Center Dahlgren Division (NSWCDD) technical reports concerning software reliability. The first report, A Survey of Software Reliability Modeling and Estimation, NSWC TR 82-171, discusses various approaches advocated for reliability estimation; reviews various models proposed for this estimation process; provides model assumptions, estimates of reliability, and the precision of those estimates; and provides the data required for the models' implementation. Eight software reliability models were selected to form the basis of a library. This library also contains data edit, transformation, general statistics, and Goodness-of-Fit functions. The original Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) Library was described in the SMERFS Library Access Guide, NSWC TR 84-371. The enhanced library, which now contains 11 models and model applicability analyses, is explained in NSWCDD TR 84-371, Revision 3. The execution of this more powerful library, through the new SMERFS driver, is explained herein. Estimation, Mean-time-between-failures, Reliability, Software errors, Software failures, Software faults, Software reliability, Software reliability models.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA275390

Entities

People

  • Oliver D. Smith
  • William H. Farr

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Accuracy
  • Central Processing Units
  • Computer Programs
  • Computers
  • Consistency
  • Data Analysis
  • Data Science
  • Data Storage Systems
  • Databases
  • Detection
  • Information Science
  • Operating Systems
  • Plastic Explosives
  • Probability
  • Standards
  • Statistics
  • Surface Warfare

Fields of Study

  • Computer science
  • Engineering

Readers

  • Library and Information Science
  • Software Engineering.
  • Statistical inference.