A Software-Reliability Growth Model for -Version Programming Systems

This paper presents a NHPP-based SRGM (software reliability growth model) for VP ( -version programming) Systems ( VP-SRGM) based on the NHPP (nonhomogeneous Poisson process). Although many papers have been devoted to modeling VP-system reliability, most of them consider only the stable reliability, i.e., they do not consider the reliability growth in VP systems due to continuous removal of faults from software versions. The model in this paper is the first reliability-growth model for VP Systems which considers the error-introduction rate and the error-removal efficiency. During testing and debug- ging, when a software fault is found, a debugging effort is devoted to remove this fault. Due to the high complexity of the software, this fault might not be successfully removed, and new faults might be introduced into the software. By applying a generalized NHPP model into VP system, a new VP-SRGM is established, in which the multi-version coincident failures are well modeled. A simplify software control logic for a water-reservoir control system illustrates how to apply this new software reliability model. The -confidence bounds are provided for system-reliability esti- mation. This software reliability model can be used to evaluate the reliability and to predict the performance of VP systems. More application is needed to validate fully the proposed VP-SRGM for quantifying the reliability of fault-tolerant software systems in a general industrial setting. As the first model of its kind in VP reliability-growth mod- eling, the proposed VP SRGM can be used to overcome the shortcomings of the independent reliability model. It predicts the system reliability more accurately than the independent model and can be used to help determine when to stop testing, which is a key question in the testing and debugging phase of the VP system-development life cycle. Index Terms—Non-homogeneous Poisson process (NHPP), -version programming ( VP), software fault-tolerance, software reliability, software reliability growth model, software testing.