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DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination

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Název: DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination
Autoři: Shikha Dwivedi, Neeraj Kumar Goyal, Hariom Chaudhari
Zdroj: International Journal of Mathematical, Engineering and Management Sciences, Vol 10, Iss 5, Pp 1192-1217 (2025)
Informace o vydavateli: Ram Arti Publishers, 2025.
Rok vydání: 2025
Témata: faults prediction, Technology, corrected faults, QA1-939, detected faults, software reliability, artificial neural networks, logarithmic encoding, Mathematics
Popis: The accurate prediction of both detected and corrected faults is crucial for enhancing software reliability and determining optimal release times. Traditional Software Reliability Growth Models (SRGMs) often focus on either fault detection or correction, potentially overlooking the comprehensive view needed for effective software maintenance. This paper introduces a Dense Neural Network (DNN)-based model that predicts both detected and corrected faults using data from the initial testing phase. The proposed model adopted a simpler architecture to reduce computational overhead and minimize time complexity, making it suitable for real-world applications. By incorporating logarithmic encoding, the model effectively manages missing data and performs well with smaller datasets, which are common in early testing stages. The proposed model is compared with existing approaches, demonstrating superior results across multiple datasets. This comparative analysis highlights the model's enhanced predictive accuracy, computational efficiency, and less time complexity. Additionally, the predicted faults are used to determine the optimal release time, based on the customer's reliability requirements and the minimum cost necessary to achieve that reliability. By offering a more comprehensive and accurate prediction of software reliability, this model provides a practical solution for software development teams, facilitating better decision-making in testing, maintenance, and release planning.
Druh dokumentu: Article
Jazyk: English
ISSN: 2455-7749
DOI: 10.33889/ijmems.2025.10.5.057
Přístupová URL adresa: https://doaj.org/article/e9cf250dbfb74843b5a6d9ff769874b1
https://doi.org/10.33889/IJMEMS.2025.10.5.057
Přístupové číslo: edsair.doi.dedup.....17550b7f28bee5ca641b7dc240e8650f
Databáze: OpenAIRE
Popis
Abstrakt:The accurate prediction of both detected and corrected faults is crucial for enhancing software reliability and determining optimal release times. Traditional Software Reliability Growth Models (SRGMs) often focus on either fault detection or correction, potentially overlooking the comprehensive view needed for effective software maintenance. This paper introduces a Dense Neural Network (DNN)-based model that predicts both detected and corrected faults using data from the initial testing phase. The proposed model adopted a simpler architecture to reduce computational overhead and minimize time complexity, making it suitable for real-world applications. By incorporating logarithmic encoding, the model effectively manages missing data and performs well with smaller datasets, which are common in early testing stages. The proposed model is compared with existing approaches, demonstrating superior results across multiple datasets. This comparative analysis highlights the model's enhanced predictive accuracy, computational efficiency, and less time complexity. Additionally, the predicted faults are used to determine the optimal release time, based on the customer's reliability requirements and the minimum cost necessary to achieve that reliability. By offering a more comprehensive and accurate prediction of software reliability, this model provides a practical solution for software development teams, facilitating better decision-making in testing, maintenance, and release planning.
ISSN:24557749
DOI:10.33889/ijmems.2025.10.5.057