Software reliability analysis models

TLDR

In practice, project teams often have only reliability data from a test report and no additional information. This paper improves conventional software reliability analysis models by making their underlying assumptions more realistic. The authors leverage technical knowledge of the program, test, and data to select appropriate models, proposing delayed S‑shaped growth, inflection S‑shaped, and hyperexponential models for accurate quality assessment. These models address the limitations imposed by scarce reliability data, enabling more accurate quality assessment.

Abstract

This paper discusses improvements to conventional software reliability analysis models by making the assumptions on which they are based more realistic. In an actual project environment, sometimes no more information is available than reliability data obtained from a test report. The models described here are designed to resolve the problems caused by this constraint on the availability of reliability data. By utilizing the technical knowledge about a program, a test, and test data, we can select an appropriate software reliability analysis model for accurate quality assessment. The delayed S-shaped growth model, the inflection S-shaped model, and the hyperexponential model are proposed.