Towards Deep Learning Models for Automatic Computer Program Grading
Bibliographic record
Abstract
Automatic grading of computer programs has a great impact on both computer science education and the software industry as it saves human evaluators a tremendous amount of time required for assessing programs. However, to date, this problem lacks extensive research from the machine learning/deep learning perspective. Currently, the traditional auto-grading systems are mostly based on test-case execution results. However, these approaches lack insight into the syntax and semantics of the codes, and therefore, are far from human-level evaluation. In this study, we leverage the power of language models pre-trained on programming languages. We introduce two simple deep architectures and show that they consistently outperform the shallow models built upon extensive feature engineering approaches by a high margin. We also develop an incremental transductive learning algorithm that only requires a single reference solution to a problem and takes advantage of the correct implementations in the set of programs to be evaluated. Furthermore, our human evaluation results show that the proposed approaches provide partial marks having a strong correlation with marks given by human graders. We prepare and share a dataset of C++ and Python programs for future research (Code and data are available at https://github.com/peter-nagy1/Deep-Grader).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".