Multi-objective Search to Design Multi-phase Model Fitting Algorithms  
Author Joshua Steakelum


Co-Author(s) Jacob Aubertine; Kenan Chen; Vidhyashree Nagaraju; Lance Fiondella


Abstract Recent research applies soft computing techniques to fit software reliability growth models. However, runtime performance and the distribution of the distance from an optimal solution over multiple runs must be explicitly considered to justify the practical utility of these approaches, promote comparison, and support reproducible research. This paper presents a metaoptimization framework to design stable and efficient multiphase algorithms for fitting software reliability growth models. The approach combines initial parameter estimation techniques from statistical algorithms, the global search properties of soft computing, and the rapid convergence of numerical methods. Designs that exhibit the best balance between runtime performance and accuracy are identified. The approach is illustrated on a nonhomogeneous Poisson process software reliability growth model, including a cross-validation step on data sets not used to identify designs. The results indicate the nonhomogeneous Poisson process model considered is too simple to benefit from soft computing because it incurs additional runtime with no increase in accuracy attained.


Keywords Software reliability, software reliability growth model, soft computing, numerical methods, multi-phase algorithms
    Article #:  RQD26-21

Proceedings of 26th ISSAT International Conference on Reliability & Quality in Design
Virtual Event

August 5-7, 2021