2026-10-14T08:10:53Z https://www.scidb.cn/oai

10.57760/sciencedb.j00207.00038 2026-01-05T11:39:25Z

2026-01-05 Statistical Inference for Software Reliability Constrained by the Shape of the Mean Value Function doi:10.57760/sciencedb.j00207.00038 en While parametric Software Reliability Growth Models (SRGMs) serve as a cornerstone in software reliability assessment, their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing. In this study, the authors  develop a novel shape-restricted spline estimator for quantifying software reliability. Compared with parametric SRGMs, the proposed estimator not only shares a key characteristic with parametric SRGMs, but also obviates the need for specifying fault-detection time distributions. More importantly, it effectively utilizes the critical shape information of the mean value function (MVF) of the fault-detection process, a detail seldom considered in prior work. Moreover, the authors  investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method. Furthermore, the authors  show that under certain conditions, the shape-restricted spline estimator will attain the point-wise convergence rate. In a numerical experiment, the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models. Software Reliability; Mean Value Function; Spline Estimator Kangan Chen Jian Liu Qingpei Hu Min Xie PUBLIC https://mit-license.org dataset http://www.doi.org/10.1007/s11424-026-4556-6 Science Data Bank