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Among the available solutions for drone swarm simulations, we identified a gap in simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis, and do not require the user to interface with multiple programming languages. We present SwarmLab, a software entirely written in Matlab, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two state-of-the-art algorithms for the navigation of aerial swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze the variability of the inter-agent distances and agents' speeds during flight. We also study some of the performance metrics presented, i.e. order, inter and extra-agent safety, union, and connectivity. While Olfati-Saber's approach results in a faster crossing of the obstacle field, Vasarhelyi's approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art.