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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.17545 |
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| _version_ | 1866914660811276288 |
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| author | Pritchard, Shadow Mitra, Bhaskar Nagaraju, Vidhyashree |
| author_facet | Pritchard, Shadow Mitra, Bhaskar Nagaraju, Vidhyashree |
| contents | Software reliability growth models (SRGM) enable failure data collected during testing. Specifically, nonhomogeneous Poisson process (NHPP) SRGM are the most commonly employed models. While software reliability growth models are important, efficient modeling of complex software systems increases the complexity of models. Increased model complexity presents a challenge in identifying robust and computationally efficient algorithms to identify model parameters and reduces the generalizability of the models. Existing studies on traditional software reliability growth models suggest that NHPP models characterize defect data as a smooth continuous curve and fail to capture changes in the defect discovery process. Therefore, the model fits well under ideal conditions, but it is not adaptable and will only fit appropriately shaped data. Neural networks and other machine learning methods have been applied to greater effect [5], however limited due to lack of large samples of defect data especially at earlier stages of testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17545 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Three-Stage Adjusted Regression Forecasting (TSARF) for Software Defect Prediction Pritchard, Shadow Mitra, Bhaskar Nagaraju, Vidhyashree Software Engineering Systems and Control Software reliability growth models (SRGM) enable failure data collected during testing. Specifically, nonhomogeneous Poisson process (NHPP) SRGM are the most commonly employed models. While software reliability growth models are important, efficient modeling of complex software systems increases the complexity of models. Increased model complexity presents a challenge in identifying robust and computationally efficient algorithms to identify model parameters and reduces the generalizability of the models. Existing studies on traditional software reliability growth models suggest that NHPP models characterize defect data as a smooth continuous curve and fail to capture changes in the defect discovery process. Therefore, the model fits well under ideal conditions, but it is not adaptable and will only fit appropriately shaped data. Neural networks and other machine learning methods have been applied to greater effect [5], however limited due to lack of large samples of defect data especially at earlier stages of testing. |
| title | Three-Stage Adjusted Regression Forecasting (TSARF) for Software Defect Prediction |
| topic | Software Engineering Systems and Control |
| url | https://arxiv.org/abs/2401.17545 |