DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination
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 vie...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ram Arti Publishers
2025-10-01
|
| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/57-IJMEMS-25-0010-10-5-1192-1217-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849329658914406400 |
|---|---|
| author | Shikha Dwivedi Neeraj Kumar Goyal Hariom Chaudhari |
| author_facet | Shikha Dwivedi Neeraj Kumar Goyal Hariom Chaudhari |
| author_sort | Shikha Dwivedi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9cf250dbfb74843b5a6d9ff769874b1 |
| institution | Kabale University |
| issn | 2455-7749 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj-art-e9cf250dbfb74843b5a6d9ff769874b12025-08-20T03:47:12ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492025-10-0110511921217https://doi.org/10.33889/IJMEMS.2025.10.5.057DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time DeterminationShikha Dwivedi0Neeraj Kumar Goyal1Hariom Chaudhari2Subir Chowdhary School of Quality and Reliability, Indian Institute of Technology, Kharagpur, West Bengal, India.Subir Chowdhary School of Quality and Reliability, Indian Institute of Technology, Kharagpur, West Bengal, India.Subir Chowdhary School of Quality and Reliability, Indian Institute of Technology, Kharagpur, West Bengal, India.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.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/57-IJMEMS-25-0010-10-5-1192-1217-2025.pdfsoftware reliabilityfaults predictionartificial neural networkslogarithmic encodingdetected faultscorrected faults |
| spellingShingle | Shikha Dwivedi Neeraj Kumar Goyal Hariom Chaudhari DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination International Journal of Mathematical, Engineering and Management Sciences software reliability faults prediction artificial neural networks logarithmic encoding detected faults corrected faults |
| title | DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination |
| title_full | DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination |
| title_fullStr | DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination |
| title_full_unstemmed | DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination |
| title_short | DNN-based Software Reliability Model for Fault Prediction and Optimal Release Time Determination |
| title_sort | dnn based software reliability model for fault prediction and optimal release time determination |
| topic | software reliability faults prediction artificial neural networks logarithmic encoding detected faults corrected faults |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/57-IJMEMS-25-0010-10-5-1192-1217-2025.pdf |
| work_keys_str_mv | AT shikhadwivedi dnnbasedsoftwarereliabilitymodelforfaultpredictionandoptimalreleasetimedetermination AT neerajkumargoyal dnnbasedsoftwarereliabilitymodelforfaultpredictionandoptimalreleasetimedetermination AT hariomchaudhari dnnbasedsoftwarereliabilitymodelforfaultpredictionandoptimalreleasetimedetermination |