The increasing energy consumption of servers, high-performance computing clusters, and data centers necessitates measures to reduce energy consumption. However, many companies, like TeamViewer, rely on rented infrastructure where direct hardware-level energy management is unavailable. This thesis presents a method for estimating software energy efficiency on virtualized server environments, focusing on optimizing code execution without access to physical hardware metrics. The research addresses several key challenges, including how to measure energy consumption at the function level of a software, simulate realistic and reproducible user loads, and considerations for isolating performance measurements from external influences. Profiling tools were evaluated to measure CPU time, a metric which is correlated with energy consumption. The method was tested in a virtual environment by simulating user loads and measuring the impact of software changes on performance. The results demonstrate that CPU time can provide insights into the performance of a software which correlates with its energy consumption. This work contributes to the field by providing a lightweight method for continuously estimating the energy efficiency during software development and maintenance.