We report on two distinct computational approaches to self-consistently measure photospheric properties of large samples of stars. Both procedures consist of a set of several semi-integrated tasks based on shell and Python scripts, which efficiently run either our own codes or open source software commonly adopted by the astronomical community. One approach aims to derive the main stellar photospheric parameters and abundances of a few elements by analysing high-resolution spectra from a given public library homogeneously constructed. The other one is applied to recover the abundance of a single element in stars with known photospheric parameters by using mid-resolution spectra from another open homogeneous database and calibrating derived abundances. Both semi-automated computational approaches provide homogeneity and objectivity to every step of the process and represent a fast way to reach partial and final results as well as to estimate measurement errors, making possible to systematically evaluate and improve the distinct steps.
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