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.

翻译：本研究论文介绍了AIOptimizer，一个基于成本降低的软件性能优化工具原型。AIOptimizer采用强化学习驱动的推荐系统，以提升软件系统的效率和经济性。论文强调了AIOptimizer的设计要素，如准确性、适应性、可扩展性和用户友好性。为提供有效且以用户为中心的性能优化解决方案，它着重于模块化设计、数据收集技术、持续学习和弹性集成。文章还探讨了AIOptimizer的功能，包括故障识别、成本优化建议、效率预测和协作。此外，它研究了几种软件开发生命周期模型，并介绍了AIOptimizer采用基于强化学习的推荐引擎进行成本优化。本研究旨在突出AIOptimizer作为一个原型，利用先进的优化技术和智能推荐系统持续提升软件性能并节约成本。研究聚焦于多种软件开发生命周期模型，如瀑布模型、迭代模型、螺旋模型、V模型、大爆炸模型和敏捷模型。每种模型各有优缺点，其适用性取决于项目的规格和特性。AIOptimizer工具是针对此类软件性能优化器的理论原型。