Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning
One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable in...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Arabic |
| Published: |
University of Information Technology and Communications
2024-12-01
|
| Series: | Iraqi Journal for Computers and Informatics |
| Subjects: | |
| Online Access: | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/509 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841558895622881280 |
|---|---|
| author | anfal A. Fadhil Asmaa’ H. AL_Bayati Ibrahim Ahmed Saleh |
| author_facet | anfal A. Fadhil Asmaa’ H. AL_Bayati Ibrahim Ahmed Saleh |
| author_sort | anfal A. Fadhil |
| collection | DOAJ |
| description | One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable influence on the overall reliability of a system is its software. Reliability is a critical aspect of software quality, and the software industry faces many challenges in its quest to produce reliable software at scale. Reliability models are a basic method for quantitatively calculating software reliability. Thus, this paper inspects the reliability of software applications as a substantial feature of this application and helps determine the extent of software reliability in performing specialized functions. This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). Results show that the SVM model achieves the best mean square error. |
| format | Article |
| id | doaj-art-1d30ea1c6eb54fa0b4dc26a52787ca6f |
| institution | Kabale University |
| issn | 2313-190X 2520-4912 |
| language | Arabic |
| publishDate | 2024-12-01 |
| publisher | University of Information Technology and Communications |
| record_format | Article |
| series | Iraqi Journal for Computers and Informatics |
| spelling | doaj-art-1d30ea1c6eb54fa0b4dc26a52787ca6f2025-01-05T22:17:48ZaraUniversity of Information Technology and CommunicationsIraqi Journal for Computers and Informatics2313-190X2520-49122024-12-0150211012110.25195/ijci.v50i2.509472Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learninganfal A. Fadhil0Asmaa’ H. AL_Bayati1Ibrahim Ahmed Saleh2University of MosulUniversity of MosulUniversity of MosulOne of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable influence on the overall reliability of a system is its software. Reliability is a critical aspect of software quality, and the software industry faces many challenges in its quest to produce reliable software at scale. Reliability models are a basic method for quantitatively calculating software reliability. Thus, this paper inspects the reliability of software applications as a substantial feature of this application and helps determine the extent of software reliability in performing specialized functions. This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). Results show that the SVM model achieves the best mean square error.https://ijci.uoitc.edu.iq/index.php/ijci/article/view/509software reliability growth modelsmachine learning; decision treek_ nearest neighborssupport vector machine |
| spellingShingle | anfal A. Fadhil Asmaa’ H. AL_Bayati Ibrahim Ahmed Saleh Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning Iraqi Journal for Computers and Informatics software reliability growth models machine learning ; decision tree k_ nearest neighbors support vector machine |
| title | Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning |
| title_full | Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning |
| title_fullStr | Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning |
| title_full_unstemmed | Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning |
| title_short | Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning |
| title_sort | develop approach to predicate software reliability growth model parameters based on machine learning |
| topic | software reliability growth models machine learning ; decision tree k_ nearest neighbors support vector machine |
| url | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/509 |
| work_keys_str_mv | AT anfalafadhil developapproachtopredicatesoftwarereliabilitygrowthmodelparametersbasedonmachinelearning AT asmaahalbayati developapproachtopredicatesoftwarereliabilitygrowthmodelparametersbasedonmachinelearning AT ibrahimahmedsaleh developapproachtopredicatesoftwarereliabilitygrowthmodelparametersbasedonmachinelearning |