Software Reliability Modeling and Analysis via Kernel-based Approach

Traditional software reliability analysis utilizes only the fault count data observed in testing phase, and is done independently of the source code itself. Recently, it is known that utilization of software metrics in software reliability modeling and analysis can lead to more accurate reliability estimation and fault prediction through many empirical studies. However, such a metrics-based modeling also requires a careful selection of software metrics and their measurement, which are often troublesome and cost-consuming in practice. In this paper, we propose a kernel-based approach to estimate the quantitative software reliability, where two cases are considered; multiple software metrics are used and not. In the former case, we combine the kernel regression with the well-known non-homogeneous Poisson process-based software reliability growth model (SRGM), and propose a new metrics-based SRGM. In the latter case, we perform a similarity-based analysis through a source code transformation algorithm and try to estimate the quantitative software reliability from the source code directly without measuring multiple software metrics. Numerical examples with real application programs are presented to validate our kernel-based approach in the above two cases.