A time/structure based software reliability model

  • Authors:

  • Swapna S. Gokhale;Kishor S. Trivedi

  • Affiliations:

  • Boruns College of Engineering, University of California, Riverside, CA 92521, USA E-mail: swapna@cs.ucr.edu;Center for Advanced Computing and Communication, Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA

  • Venue:
  • Annals of Software Engineering
  • Year:
  • 1999

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Abstract

The past 20 years have seen the formulation of numerous analytical software reliability models for estimating the reliability growth of a software product. The predictions obtained by applying these models tend to be optimistic due to the inaccuracies in the operational profile, and saturation effect of testing. Incorporating knowledge gained about some structural attribute of the code, such as test coverage, into the time‐domain models can help alleviate this optimistic trend. In this paper we present an enhanced non‐homogeneous Poisson process (ENHPP) model which incorporates explicitly the time‐varying test‐coverage function in its analytical formulation, and provides for defective fault detection and test coverage during the testing and operational phases. It also allows for a time varying fault detection rate. The ENHPP model offers a unifying framework for all the previously reported finite failure NHPP models via test coverage. We also propose the log‐logistic coverage function which can capture an increasing/decreasing failure detection rate per fault, which cannot be accounted for by the previously reported finite failure NHPP models. We present a methodology based on the ENHPP model for reliability prediction earlier in the testing phase. Expressions for predictions in the operational phase of the software, software availability, and optimal software release times subject to various constraints such as cost, reliability, and availability are developed based on the ENHPP model. We also validate the ENHPP model based on four different coverage functions using five failure data sets.