Modeling Software Reliability Growth with Learning and Fatigue Effects.

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Bibliographic Details
Title:	Modeling Software Reliability Growth with Learning and Fatigue Effects.
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]
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Database:	Engineering Source

Description
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]
ISSN:	10162364
DOI:	10.6688/JISE.20260142(1).0001