// TODO: Help students improve commenting practices
Bibliographic record
Abstract
One implicit purpose of writing software code is to communicate ideas. Commenting source code helps explain these ideas and provides background on the semantics of a program. Yet, enabling students to acquire good commenting practices remains difficult. Instructors can find it hard to meaningfully discuss such practices in both introductory and advanced undergraduate courses. Furthermore, comment grading is an imprecise, labor-intensive procedure at best. But just what practices should we be encouraging students to emulate? To help address these issues (learning about professional code commenting patterns and best practices, objectively grading student comments), we developed the COMTOR tool as an open source project and web service. COMTOR provides a platform for helping assess source code documentation in an objective, structured fashion. We conducted two experiments using COMTOR: one that examines a set of popular open-source Java code projects and one that measures a baseline of student-generated code comments. The latter are based on three semesters worth of data from two different undergraduate courses. Our aim is to extract knowledge about the state of professional practice in source code comments and how these properties vary over the lifetime of a project. We can then begin to make recommendations and communicate best practices (or a lack thereof) to our students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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 source (direct Gemma or distilled Codex), 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".