Static Analyses In Python Programming Courses

Students learning to program often rely on feedback from the compiler and from instructor-provided test cases to help them identify errors in their code. This feedback focuses on functional correctness, and the output, which is often phrased in technical language, may be difficult to for novices to understand or effectively use. Static analyses may be effective as a complementary aid, as they can highlight common errors that may be potential sources of problems. In this paper, we introduce PyTA, a wrapper for pylint that provides custom checks for common novice errors as well as improved messages to help students fix the errors that are found. We report on our experience integrating PyTA into an existing online system used to deliver programming exercises to CS1 students and evaluate it by comparing exercise submissions collected from the integrated system to previously collected data. This analysis demonstrates that, for students who chose to read the PyTA output, we observed a decrease in time to solve errors, occurrences of repeated errors, and submissions to complete a programming problem. This suggests that PyTA, and static analyses in general, may help students identify functional issues in their code not highlighted by compiler feedback and that static analysis output may help students more quickly identify debug their code.