The dataset comprises 12,000 files, each containing approximately 50 lines of code written in
31 programming languages. Figure 1 shows the distribution of programming languages in the dataset.
To increase the representation of rare PII types, such as keys and IP addresses, 7,100 files were
pre-filtered from a larger sample. We utilized the detect-secrets tool10 with all default plugins
activated, along with the regular expressions by Ben Allal et al. (2023) for detecting emails, IPv4
and IPv6 addresses. To prevent biasing the annotation too much towards these detection tools, the
remaining 5,100 files were randomly selected from the dataset without pre-filtering.
During annotation, we differentiated between various types of PII based on the specific context in
which it appeared. Specifically, we distinguished whether the PII was present in the code’s license
header, was used as a placeholder, or constituted confidential data. This categorization was necessary
StarCoder_paper (1)