Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties.
Saved in:
| 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] |
| Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194811386 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khan%2C+Farhan+Mateen%22">Khan, Farhan Mateen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Munir%2C+Asim%22">Munir, Asim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ali%2C+Shujaat%22">Ali, Shujaat</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jameel%2C+Tahir%22">Jameel, Tahir</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khan%2C+Mohammed+Asmat+Ullah%22">Khan, Mohammed Asmat Ullah</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shah%2C+Dilawar%22">Shah, Dilawar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tahir%2C+Muhammad%22">Tahir, Muhammad</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> m.tahir@kardan.edu.af</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Software%3A+Evolution+%26+Process%22">Journal of Software: Evolution & Process</searchLink>. Jun2026, Vol. 38 Issue 6, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Software+failures%22">Software failures</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+processes%22">Poisson processes</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194811386 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/smr.70120 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Software reliability Type: general – SubjectFull: Software failures Type: general – SubjectFull: Poisson processes Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Computer software testing Type: general Titles: – TitleFull: Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khan, Farhan Mateen – PersonEntity: Name: NameFull: Munir, Asim – PersonEntity: Name: NameFull: Ali, Shujaat – PersonEntity: Name: NameFull: Jameel, Tahir – PersonEntity: Name: NameFull: Khan, Mohammed Asmat Ullah – PersonEntity: Name: NameFull: Shah, Dilawar – PersonEntity: Name: NameFull: Tahir, Muhammad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20477473 Numbering: – Type: volume Value: 38 – Type: issue Value: 6 Titles: – TitleFull: Journal of Software: Evolution & Process Type: main |
| ResultId | 1 |
