As Dempster-Shafer theory spreads in different applications fields involving complex systems, the need for algorithms randomly generating mass functions arises. As such random generation is often perceived as secondary, most proposed algorithms use procedures whose sample statistical properties are difficult to characterize. Thus, although they produce randomly generated mass functions, it is difficult to control the sample statistical laws. In this paper, we briefly review classical algorithms, explaining why their statistical properties are hard to characterize, and then provide simple procedures to perform efficient and controlled random generation.