Style Anomalies Can Suggest Cheating in CS1 Programs

Student cheating on at-home programming assignments is a well- known problem. A key contributor is externally-obtained solutions from websites, contractors, and recently generative AI. In our experience, such externally-obtained solutions often use coding styles that depart from a class' style, which we call "style anomalies," such as using untaught or advanced constructs like pointers or ternary operators, or having different indenting or brace usage from the class style. We developed a tool to auto-count style anomalies. For six labs across four terms in 2021-2022, and 50 sampled students per lab, we found 18% of submissions on average had unusually-high style anomaly counts. Importantly, 8% of submissions on average had a high style anomaly count but were not flagged by a similarity checker, meaning 8% of submissions are suspicious but might have been missed if using similarity checking alone. We repeated a similar analysis for Spring 2023 when generative AI (ChatGPT) was gaining popularity, and the numbers rose to 26% and 18%, respectively. Detailed investigations by instructors led to a majority (but not all) high style anomaly submissions being deemed cheating. Even for high-similarity submissions, counting style anomalies can help instructors focus investigations on the most-likely cheating cases, and can strengthen cases sent to student conduct offices. With the rise of externally-obtained solutions from websites, contractors, and generative AI, counting style anomalies may become an increasingly important complement to similarity checking; in fact, it is now the primary cheat-detection tool in our CS1 at a large state university, with similarity secondary.