  Multilingualism is widespread around the world and code-switching (CSW) is a
common practice among different language pairs/tuples across locations and
regions. However, there is still not much progress in building successful CSW
systems, despite the recent advances in Massive Multilingual Language Models
(MMLMs). We investigate the reasons behind this setback through a critical
study about the existing CSW data sets (68) across language pairs in terms of
the collection and preparation (e.g. transcription and annotation) stages. This
in-depth analysis reveals that \textbf{a)} most CSW data involves English
ignoring other language pairs/tuples \textbf{b)} there are flaws in terms of
representativeness in data collection and preparation stages due to ignoring
the location based, socio-demographic and register variation in CSW. In
addition, lack of clarity on the data selection and filtering stages shadow the
representativeness of CSW data sets. We conclude by providing a short
check-list to improve the representativeness for forthcoming studies involving
CSW data collection and preparation.
