Early Phase Software Dependability Analysis: A Neutrosophic Inference System-Based Approach.
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| Title: | Early Phase Software Dependability Analysis: A Neutrosophic Inference System-Based Approach. |
|---|---|
| Authors: | Chatterjee, Subhashis1 (AUTHOR) subhashis@iitism.ac.in, Saha, Deepjyoti1 (AUTHOR) sahadeepjyoti01@gmail.com, Sharma, Akhilesh2 (AUTHOR) akhil2678@gmail.com, Verma, Yogesh2 (AUTHOR) yogeshverma@sac.isro.gov.in |
| Source: | International Journal of Reliability, Quality & Safety Engineering. Oct2025, Vol. 32 Issue 5, p1-29. 29p. |
| Subjects: | Artificial neural networks, Computer software developers, Computer software quality control, Computer software development, Systems software |
| Abstract: | Due to the presence of reliability, security, or performance-related issues, software systems will become nondependable during the early development phase. Currently, there is a lack of research addressing these dependability issues in software quality analysis. To bridge this gap, this study proposes a neutrosophic inference system (NIS)-based model to predict reliability, security and performance attributes during the early phase. The NIS model accommodates uncertainties, imprecisions, indeterminacies and incompleteness of metric values utilizing its truth, indeterminate and false components. To enhance the prediction accuracy of the NIS model, a rule-base formation algorithm is proposed for NIS considering domain expert knowledge. Finally, an artificial neural network (ANN) model is designed based on estimated values of reliability, security and performance attributes to predict the total number of faults in software projects. Comparative analysis demonstrates that the proposed model outperforms other existing models. This proposed methodology helps software developers in assessing software dependability from the beginning stage of software development. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Reliability, Quality & Safety Engineering is the property of World Scientific Publishing Company 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188096993 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Early Phase Software Dependability Analysis: A Neutrosophic Inference System-Based Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chatterjee%2C+Subhashis%22">Chatterjee, Subhashis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> subhashis@iitism.ac.in</i><br /><searchLink fieldCode="AR" term="%22Saha%2C+Deepjyoti%22">Saha, Deepjyoti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sahadeepjyoti01@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sharma%2C+Akhilesh%22">Sharma, Akhilesh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> akhil2678@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Verma%2C+Yogesh%22">Verma, Yogesh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yogeshverma@sac.isro.gov.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Reliability%2C+Quality+%26+Safety+Engineering%22">International Journal of Reliability, Quality & Safety Engineering</searchLink>. Oct2025, Vol. 32 Issue 5, p1-29. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+developers%22">Computer software developers</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+software%22">Systems software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Due to the presence of reliability, security, or performance-related issues, software systems will become nondependable during the early development phase. Currently, there is a lack of research addressing these dependability issues in software quality analysis. To bridge this gap, this study proposes a neutrosophic inference system (NIS)-based model to predict reliability, security and performance attributes during the early phase. The NIS model accommodates uncertainties, imprecisions, indeterminacies and incompleteness of metric values utilizing its truth, indeterminate and false components. To enhance the prediction accuracy of the NIS model, a rule-base formation algorithm is proposed for NIS considering domain expert knowledge. Finally, an artificial neural network (ANN) model is designed based on estimated values of reliability, security and performance attributes to predict the total number of faults in software projects. Comparative analysis demonstrates that the proposed model outperforms other existing models. This proposed methodology helps software developers in assessing software dependability from the beginning stage of software development. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Reliability, Quality & Safety Engineering is the property of World Scientific Publishing Company 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218539324500529 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer software developers Type: general – SubjectFull: Computer software quality control Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Systems software Type: general Titles: – TitleFull: Early Phase Software Dependability Analysis: A Neutrosophic Inference System-Based Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chatterjee, Subhashis – PersonEntity: Name: NameFull: Saha, Deepjyoti – PersonEntity: Name: NameFull: Sharma, Akhilesh – PersonEntity: Name: NameFull: Verma, Yogesh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02185393 Numbering: – Type: volume Value: 32 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Reliability, Quality & Safety Engineering Type: main |
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