This research article introduces AIOptimizer, a prototype for a software
performance optimisation tool based on cost reduction. AIOptimizer uses a
recommendation system driven by reinforcement learning to improve software
system efficiency and affordability. The paper highlights AIOptimizer's design
factors, such as accuracy, adaptability, scalability, and user-friendliness. To
provide effective and user-centric performance optimisation solutions, it
emphasises the use of a modular design, data gathering techniques, continuous
learning, and resilient integration. The article also investigates AIOptimizer
features such as fault identification, cost optimisation recommendations,
efficiency prediction, and cooperation. Furthermore, it explores several
software development life cycle models and introduces AIOptimizer uses a
reinforcement learning-based recommendation engine for cost optimisation. The
purpose of this research study is to highlight AIOptimizer as a prototype that
uses advanced optimisation techniques and smart recommendation systems to
continually enhance software performance and save expenses. The research
focuses on various software development life cycle models, such as the
Waterfall model, Iterative model, Spiral model, V-Model, Big Bang model and
Agile Model. Each model has advantages and disadvantages, and their usefulness
is determined by the project's specifications and characteristics. The
AIOptimizer tool is a theoretical prototype for such software performance
optimizers.

AIOptimizer 是一个使用强化学习控制的推荐系统，通过数据收集技术、持续学习和有弹性的融合设计等因素来提高软件系统效率和可负担性；研究重点在于各种软件开发生命周期模型，AIOptimizer 是一个理论性的原型工具。