Self-Exciting Software Reliability Models with Pareto Base Intensity Function and Their Applications
Nanxiang Qiu, Tadashi Dohi, Junjun Zheng, Hiroyuki Okamura · 2025
Existing software reliability models (SRMs) that describe software fault detection during the testing phase can be unified under the framework of self-exciting point processes. However, it remains unclear whether such generalized self-excitation models can outperform traditional non-homogeneous Poisson process (NHPP) and non-homogeneous Markov process (NHMP)-based SRMs in terms of goodness-of-fit and predictive capabilities. In this paper, we propose a family of Hawkes process (HKP)-based SRMs that incorporate time-varying base intensity functions, distinguishing them from conventional HKPs with constant base intensity. Specifically, we introduce a Pareto-type base intensity and explore ten different impact (kernel) functions in the stochastic intensity part, and then compare the performance of the proposed HKP-based models with that of representative NHPP and NHMP-based SRMs. Experimental results on eight software development project datasets demonstrate that the HKP-based models with self-excitation generally achieve superior goodness-of-fit and predictive performance.