A Retrospective Study to Understand Student Coding Style Quality via Static Analysis
Bibliographic record
Abstract
This research full paper carries out an investigation of student code quality regarding coding style conventions with the support of static analyzers (SA). In programming courses, students need to learn both about code correctness and quality. Autograders have been used to support checking correctness of student code, but quality has mostly been ignored. SAs may be used to provide reports on code quality, such as checking whether students abide by coding standards and adequate coding style. Related work on student code quality in programming courses is scarce, especially in CS2 courses. The study analyzes a code dataset from 496 students from 10 different CS2 classes, and describes coding style issues both qualitatively and quantitatively. From prevalent style issues, we derived four metrics to summarize coding style problems related to formatting and naming identifiers. Then, we characterized student coding style to understand its relation with autograder student performance. Results provide more thorough understanding of student code quality regarding style, which may be useful to both researchers and instructors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".