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This research article introduces AIOptimizer, a prototype for a softwareperformance optimisation tool based on cost reduction. AIOptimizer uses arecommendation system driven by reinforcement learning to improve softwaresystem efficiency and affordability. The paper highlights AIOptimizer's designfactors, such as accuracy, adaptability, scalability, and user-friendliness. Toprovide effective and user-centric performance optimisation solutions, itemphasises the use of a modular design, data gathering techniques, continuouslearning, and resilient integration. The article also investigates AIOptimizerfeatures such as fault identification, cost optimisation recommendations,efficiency prediction, and cooperation. Furthermore, it explores severalsoftware development life cycle models and introduces AIOptimizer uses areinforcement learning-based recommendation engine for cost optimisation. Thepurpose of this research study is to highlight AIOptimizer as a prototype thatuses advanced optimisation techniques and smart recommendation systems tocontinually enhance software performance and save expenses. The researchfocuses on various software development life cycle models, such as theWaterfall model, Iterative model, Spiral model, V-Model, Big Bang model andAgile Model. Each model has advantages and disadvantages, and their usefulnessis determined by the project's specifications and characteristics. TheAIOptimizer tool is a theoretical prototype for such software performanceoptimizers.

AIOptimizer -- A reinforcement learning-based software performance optimisation prototype for cost minimisation