{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:40:48Z","timestamp":1760233248370,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>In the literature, infinite-failure software reliability models (SRMs), such as Musa-Okumoto SRM (1984), have been demonstrated to be effective in quantitatively characterizing software testing processes and assessing software reliability. This paper primarily focuses on the infinite-failure (type-II) non-homogeneous Poisson process (NHPP)-based SRMs and evaluates the performances of these SRMs comprehensively by comparing with the existing finite-failure (type-I) NHPP-based SRMs. In more specific terms, to describe the software fault-detection time distribution, we postulate 11 representative probability distribution functions that can be categorized into the generalized exponential distribution family and the extreme-value distribution family. Then, we compare the goodness-of-fit and predictive performances with the associated 11 type-I and type-II NHPP-based SRMs. In numerical experiments, we analyze software fault-count data, collected from 16 actual development projects, which are commonly known in the software industry as fault-count time-domain data and fault-count time-interval data (group data). The maximum likelihood method is utilized to estimate the model parameters in both NHPP-based SRMs. In a comparison of the type-I with the type-II, it is shown that the type-II NHPP-based SRMs could exhibit better predictive performance than the existing type-I NHPP-based SRMs, especially in the early stage of software testing.<\/jats:p>","DOI":"10.3390\/software2010001","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T07:33:33Z","timestamp":1672126413000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Are Infinite-Failure NHPP-Based Software Reliability Models Useful?"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5275-673X","authenticated-orcid":false,"given":"Siqiao","family":"Li","sequence":"first","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8511, Japan"}]},{"given":"Tadashi","family":"Dohi","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8511, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6881-0593","authenticated-orcid":false,"given":"Hiroyuki","family":"Okamura","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8511, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1080\/01621459.1996.10476944","article-title":"Bayesian computation for nonhomogeneous Poisson processes in software reliability","volume":"91","author":"Kuo","year":"1996","journal-title":"J. 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