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One problem when studying how to find and fix syntax errors is how to getnatural and representative examples of syntax errors. Most syntax errordatasets are not free, open, and public, or they are extracted from noviceprogrammers and do not represent syntax errors that the general population ofdevelopers would make. Programmers of all skill levels post questions andanswers to Stack Overflow which may contain snippets of source code along withcorresponding text and tags. Many snippets do not parse, thus they are ripe forforming a corpus of syntax errors and corrections. Our primary contribution isan approach for extracting natural syntax errors and their corresponding humanmade fixes to help syntax error research. A Python abstract syntax tree parseris used to determine preliminary errors and corrections on code blocksextracted from the SOTorrent data set. We further analyzed our code byexecuting the corrections in a Python interpreter. We applied our methodologyto produce a public data set of 62,965 Python Stack Overflow code snippets withcorresponding tags, errors, and stack traces. We found that errors made byStack Overflow users do not match errors made by student developers or randommutations, implying there is a serious representativeness risk within thefield. Finally we share our dataset openly so that future researchers canre-use and extend our syntax errors and fixes.

Syntax and Stack Overflow: A methodology for extracting a corpus of syntax errors and fixes