An Empirical Method for Selecting Software Reliability Growth Models

  • Authors:

  • C. Stringfellow;A. Amschler Andrews

  • Affiliations:

  • Computer Science Department, Midwestern State University, Wichita Falls, TX 76308/ stringfellow@mwsu.edu;School of Electrical Engineering and Computer Science, Washingron State University, Pullman, WA 99164/ aandrews@eecs.wsu.edu

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2002

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Abstract

Estimating remaining defects (or failures) in software can help test managers make release decisions during testing. Several methods exist to estimate defect content, among them a variety of software reliability growth models (SRGMs). SRGMs have underlying assumptions that are often violated in practice, but empirical evidence has shown that many are quite robust despite these assumption violations. The problem is that, because of assumption violations, it is often difficult to know which models to apply in practice. We present an empirical method for selecting SRGMs to make release decisions. The method provides guidelines on how to select among the SRGMs to decide on the best model to use as failures are reported during the test phase. The method applies various SRGMs iteratively during system test. They are fitted to weekly cumulative failure data and used to estimate the expected remaining number of failures in software after release. If the SRGMs pass proposed criteria, they may then be used to make release decisions. The method is applied in a case study using defect reports from system testing of three releases of a large medical record system to determine how well it predicts the expected total number of failures.