A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction
Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today&...
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2022-07-01
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| Series: | Journal of Universal Computer Science |
| Online Access: | https://lib.jucs.org/article/80537/download/pdf/ |
| _version_ | 1811222152465088512 |
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| author | Ajay Kumar |
| author_facet | Ajay Kumar |
| author_sort | Ajay Kumar |
| collection | DOAJ |
| description | Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction. |
| first_indexed | 2024-04-12T08:12:50Z |
| format | Article |
| id | doaj.art-c8123bf04a0949aebbe52f506726d983 |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-04-12T08:12:50Z |
| publishDate | 2022-07-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-c8123bf04a0949aebbe52f506726d9832022-12-22T03:40:56ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682022-07-0128770873210.3897/jucs.8053780537A Neuro-Fuzzy Hybridized Approach for Software Reliability PredictionAjay Kumar0Assistant Professor, Department of Computer Science and Engineering, Ajay Kumar Garg Engineering CollegeContext: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction.https://lib.jucs.org/article/80537/download/pdf/ |
| spellingShingle | Ajay Kumar A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction Journal of Universal Computer Science |
| title | A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction |
| title_full | A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction |
| title_fullStr | A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction |
| title_full_unstemmed | A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction |
| title_short | A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction |
| title_sort | neuro fuzzy hybridized approach for software reliability prediction |
| url | https://lib.jucs.org/article/80537/download/pdf/ |
| work_keys_str_mv | AT ajaykumar aneurofuzzyhybridizedapproachforsoftwarereliabilityprediction AT ajaykumar neurofuzzyhybridizedapproachforsoftwarereliabilityprediction |