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In this work, we developed an algorithm for detecting code quality issues inthe templates of online programming tasks, validated it, and conducted anempirical study on the dataset of student solutions. The algorithm consists ofanalyzing recurring unfixed issues in solutions of different students, matchingthem with the code of the template, and then filtering the results. Our manualvalidation on a subset of tasks demonstrated a precision of 80.8% and a recallof 73.3%. We used the algorithm on 415 Java tasks from the JetBrains Academyplatform and discovered that as much as 14.7% of tasks have at least one issuein their template, thus making it harder for students to learn good codequality practices. We describe our results in detail, provide severalmotivating examples and specific cases, and share the feedback of thedevelopers of the platform, who fixed 51 issues based on the output of ourapproach.

Detecting Code Quality Issues in Pre-written Templates of Programming Tasks in Online Courses