Modeling Software Reliability Growth with Learning and Fatigue Effects.
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| Title: | Modeling Software Reliability Growth with Learning and Fatigue Effects. |
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| Authors: | SAMAL, UMASHANKAR1 umashankar.samal249@gmail.com |
| Source: | Journal of Information Science & Engineering. Jan2026, Vol. 42 Issue 1, p1-12. 12p. |
| Subjects: | Software reliability, Learning curve, Learning, Computer software testing, Fatigue (Physiology), Debugging, Quality control |
| Abstract: | Software reliability is a fundamental pillar for ensuring the quality and dependability of software systems. Traditionally, software errors were commonly associated with coding mistakes. However, recent insights have shed light on the fact that human error is not static; rather, it is influenced by dynamic elements such as the processes of learning and the impact of fatigue. This study presents an approach that factors in the fatigue experienced by software testers during the debugging process, resulting in more reasonable software reliability growth models (SRGMs). By integrating S-shaped learning curves alongside an exponential function to model tester fatigue, the proposed models offer a more realistic representation of reliability growth over time. The models’ quality, predictive capabilities, and accuracy are assessed to other existing models using three established fit criteria and two commonly used datasets. By including the fatigue component into SRGMs, a more comprehensive representation of software reliability dynamics is achieved. The inclusion of this feature enhances the precision and prediction capabilities of the models, therefore facilitating a more realistic evaluation of the long-term reliability of the software system. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica 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: 190869265 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling Software Reliability Growth with Learning and Fatigue Effects. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22SAMAL%2C+UMASHANKAR%22">SAMAL, UMASHANKAR</searchLink><relatesTo>1</relatesTo><i> umashankar.samal249@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Information+Science+%26+Engineering%22">Journal of Information Science & Engineering</searchLink>. Jan2026, Vol. 42 Issue 1, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+curve%22">Learning curve</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Fatigue+%28Physiology%29%22">Fatigue (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Debugging%22">Debugging</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software reliability is a fundamental pillar for ensuring the quality and dependability of software systems. Traditionally, software errors were commonly associated with coding mistakes. However, recent insights have shed light on the fact that human error is not static; rather, it is influenced by dynamic elements such as the processes of learning and the impact of fatigue. This study presents an approach that factors in the fatigue experienced by software testers during the debugging process, resulting in more reasonable software reliability growth models (SRGMs). By integrating S-shaped learning curves alongside an exponential function to model tester fatigue, the proposed models offer a more realistic representation of reliability growth over time. The models’ quality, predictive capabilities, and accuracy are assessed to other existing models using three established fit criteria and two commonly used datasets. By including the fatigue component into SRGMs, a more comprehensive representation of software reliability dynamics is achieved. The inclusion of this feature enhances the precision and prediction capabilities of the models, therefore facilitating a more realistic evaluation of the long-term reliability of the software system. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica 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: 10.6688/JISE.20260142(1).0001 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Software reliability Type: general – SubjectFull: Learning curve Type: general – SubjectFull: Learning Type: general – SubjectFull: Computer software testing Type: general – SubjectFull: Fatigue (Physiology) Type: general – SubjectFull: Debugging Type: general – SubjectFull: Quality control Type: general Titles: – TitleFull: Modeling Software Reliability Growth with Learning and Fatigue Effects. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: SAMAL, UMASHANKAR IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10162364 Numbering: – Type: volume Value: 42 – Type: issue Value: 1 Titles: – TitleFull: Journal of Information Science & Engineering Type: main |
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