Are a Static Analysis Tool Study's Findings Static? A Replication

In 2017, Edwards et al. studied a large corpus of Java programs collected through an automated submission and assessment system that integrated static analysis feedback. They found that errors reported were most commonly related to formatting, but that the frequency of errors they categorized as "Coding Flaws" correlated with program correctness grades. They argued that static analysis feedback could detect problems relating to code correctness and could therefore be useful beyond evaluating conformance to style rules, but that students may overlook non-cosmetic error messages because of the relative volume of formatting errors. In this paper we perform a conceptual replication of the Edwards et al. study with 1270 CS1 students learning Python. We confirm that almost a decade later and even after being instructed to use the auto-formatting options within their IDE, students still encounter mostly formatting errors when using a static analysis tool. We find that the second-most common category of errors detected are "Coding Flaws", and, like Edwards et al., that the frequency of coding flaws identified by the static analysis tool correlates to program correctness. When we examine trends based on levels of prior programming experience, we find that all students tend to make more formatting errors than other kinds of errors, but that students with no prior programming experience have more errors reported across all error categories.