Denny Zhou, Nathanael Sch¨arli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans,
Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. Least-to-most prompting enables complex
reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022. (cited on p. 29)

Terry Yue Zhuo. Large language models are state-of-the-art evaluators of code generation, 2023.

(cited on p. 33)

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A G I T H U B I S S U E S F I LT E R I N G

Below we present the filters and regular expressions we used for the GitHub issues.

# regexes used for removing automated text
GITHUB_EMAILS = [

re.compile(pattern, re.DOTALL)
for pattern in [

"(.*)From:.+Reply to this email directly.+view it on GitHub(.*)\n?(.*)",
"(.*)On.+notifications@github.com.+wrote:.+Reply to this email directly.+view it

on GitHub(.*)\n?(.*)",

"(.*)Signed-off-by: .+<.+>(.*?)\n?(.*)",

]

]
GITHUB_EMAIL_DATE = re.compile("\d+/\d+/\d+ \d{2}:\d{2} [AP]M.+wrote")
GITHUB_EMAIL_LINEBREAK = re.compile("_{20,}")
StarCoder_paper (1)