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Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties.

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Title: Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties.
Authors: Khan, Farhan Mateen1 (AUTHOR), Munir, Asim1 (AUTHOR), Ali, Shujaat2 (AUTHOR), Jameel, Tahir3 (AUTHOR), Khan, Mohammed Asmat Ullah1,4 (AUTHOR), Shah, Dilawar2 (AUTHOR), Tahir, Muhammad5 (AUTHOR) m.tahir@kardan.edu.af
Source: Journal of Software: Evolution & Process. Jun2026, Vol. 38 Issue 6, p1-19. 19p.
Subjects: Software reliability, Software failures, Poisson processes, Maximum likelihood statistics, Computer software development, Computer software testing
Abstract: Software reliability growth models are essential for assessing software quality, predicting failure behavior, and supporting release planning during the software development lifecycle. However, many existing models rely on simplifying assumptions, such as independent failures, perfect debugging, and fixed operational conditions, which may limit their predictive accuracy in real‐world software systems. To address these limitations, this study proposes a generalized Software Reliability Growth Model based on the Non‐Homogeneous Poisson Process framework. The proposed model integrates dependent failure behavior, testing coverage, fault detection intensity, repair intensity, and uncertainty in operational usage profiles. By incorporating fault dependency dynamics into the mean value function, the model provides a more realistic representation of how software failures occur and evolve during testing. The model parameters are estimated using the Maximum Likelihood Estimation method, and its performance is evaluated using 2 real‐world software failure datasets. Comparative analysis is conducted against several established independent and dependent failure models using multiple goodness‐of‐fit and predictive accuracy criteria, including Mean Squared Error, Mean Absolute Error, Adjusted R‐squared, Akaike Information Criterion, Root Mean Square Prediction Error, Predictive Power, Predictive Ratio Risk, and Theil Statistic. The results show that the proposed model achieves superior or highly competitive performance across both datasets, particularly in terms of error reduction and model fit. In addition, an optimal software release‐time framework is developed to examine the effect of testing cost, error removal cost, installation cost, failure penalty cost, and expected usage duration on release decisions. The findings indicate that the proposed model can support more accurate reliability assessment, cost‐sensitive release planning, and improved software quality management. Overall, the study provides a practical and analytically flexible framework for modeling software reliability under dependent failures, testing coverage variation, and operational uncertainty. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Software reliability growth models are essential for assessing software quality, predicting failure behavior, and supporting release planning during the software development lifecycle. However, many existing models rely on simplifying assumptions, such as independent failures, perfect debugging, and fixed operational conditions, which may limit their predictive accuracy in real‐world software systems. To address these limitations, this study proposes a generalized Software Reliability Growth Model based on the Non‐Homogeneous Poisson Process framework. The proposed model integrates dependent failure behavior, testing coverage, fault detection intensity, repair intensity, and uncertainty in operational usage profiles. By incorporating fault dependency dynamics into the mean value function, the model provides a more realistic representation of how software failures occur and evolve during testing. The model parameters are estimated using the Maximum Likelihood Estimation method, and its performance is evaluated using 2 real‐world software failure datasets. Comparative analysis is conducted against several established independent and dependent failure models using multiple goodness‐of‐fit and predictive accuracy criteria, including Mean Squared Error, Mean Absolute Error, Adjusted R‐squared, Akaike Information Criterion, Root Mean Square Prediction Error, Predictive Power, Predictive Ratio Risk, and Theil Statistic. The results show that the proposed model achieves superior or highly competitive performance across both datasets, particularly in terms of error reduction and model fit. In addition, an optimal software release‐time framework is developed to examine the effect of testing cost, error removal cost, installation cost, failure penalty cost, and expected usage duration on release decisions. The findings indicate that the proposed model can support more accurate reliability assessment, cost‐sensitive release planning, and improved software quality management. Overall, the study provides a practical and analytically flexible framework for modeling software reliability under dependent failures, testing coverage variation, and operational uncertainty. [ABSTRACT FROM AUTHOR]
ISSN:20477473
DOI:10.1002/smr.70120