AIOptimizer - Software performance optimisation prototype for cost minimisation
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913499588853760 |
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| author | Zambare, Noopur |
| author_facet | Zambare, Noopur |
| contents | This study presents AIOptimizer, a prototype for a cost-reduction-based software performance optimisation tool. The study focuses on the design elements of AIOptimizer, including user-friendliness, scalability, accuracy, and adaptability. To deliver efficient and user-focused performance optimisation solutions, it promotes the use of robust integration, continuous learning, modular design, and data collection methods. The paper also looks into AIOptimizer features including collaboration, efficiency prediction, cost optimisation suggestions, and fault diagnosis. Additionally, it introduces AIOptimizer, a recommendation engine for cost optimisation based on reinforcement learning, and examines several software development life cycle models. The goal of this research study is to showcase AIOptimizer as a prototype that continuously improves software performance and reduces costs by utilising sophisticated optimisation techniques and intelligent recommendation systems. Numerous software development life cycle models, including the Big Bang, V-, Waterfall, Iterative, and Agile models are the subject of the study. Every model has benefits and drawbacks, and the features and requirements of the project will decide how useful each is. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_07846 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AIOptimizer - Software performance optimisation prototype for cost minimisation Zambare, Noopur Software Engineering Artificial Intelligence This study presents AIOptimizer, a prototype for a cost-reduction-based software performance optimisation tool. The study focuses on the design elements of AIOptimizer, including user-friendliness, scalability, accuracy, and adaptability. To deliver efficient and user-focused performance optimisation solutions, it promotes the use of robust integration, continuous learning, modular design, and data collection methods. The paper also looks into AIOptimizer features including collaboration, efficiency prediction, cost optimisation suggestions, and fault diagnosis. Additionally, it introduces AIOptimizer, a recommendation engine for cost optimisation based on reinforcement learning, and examines several software development life cycle models. The goal of this research study is to showcase AIOptimizer as a prototype that continuously improves software performance and reduces costs by utilising sophisticated optimisation techniques and intelligent recommendation systems. Numerous software development life cycle models, including the Big Bang, V-, Waterfall, Iterative, and Agile models are the subject of the study. Every model has benefits and drawbacks, and the features and requirements of the project will decide how useful each is. |
| title | AIOptimizer - Software performance optimisation prototype for cost minimisation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2307.07846 |