The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its successful execution. This resource allocation influences both the operational expense and the performance quality of these functions. However, a noticeable lack of platform transparency forces developers to rely on expert knowledge or experience-based ad-hoc decisions to request desired function resources. This makes optimal resource configuration a non-trivial task while adhering to performance constraints. Furthermore, while commercial platforms often scale resources like CPU and network bandwidth proportional to memory, open-source frameworks permit independent configuration of function resources, introducing additional complexity for developers aiming to optimise their functions. These complexities have directed researchers to resolve developer challenges and advance towards an efficient server-less execution model. In this article, we identify different aspects of resource configuration techniques in FaaS settings and propose a taxonomy of factors that influence function design, configuration, run-time cost, and performance guarantees. We conduct an analysis of existing literature on resource configuration to present a comprehensive review of current studies on function configuration. We also identify existing research gaps and suggest future research directions to enhance function configuration and strengthen the capabilities of serverless computing environments to drive its broader adoption.