Syntax and sensibility: Using language models to detect and correct syntax errors
Bibliographic record
Abstract
Syntax errors are made by novice and experienced programmers alike; however, novice programmers lack the years of experience that help them quickly resolve these frustrating errors. Standard LR parsers are of little help, typically resolving syntax errors and their precise location poorly. We propose a methodology that locates where syntax errors occur, and suggests possible changes to the token stream that can fix the error identified. This methodology finds syntax errors by using language models trained on correct source code to find tokens that seem out of place. Fixes are synthesized by consulting the language models to determine what tokens are more likely at the estimated error location. We compare n-gram and LSTM (long short-term memory) language models for this task, each trained on a large corpus of Java code collected from GitHub. Unlike prior work, our methodology does not rely that the problem source code comes from the same domain as the training data. We evaluated against a repository of real student mistakes. Our tools are able to find a syntactically-valid fix within its top-2 suggestions, often producing the exact fix that the student used to resolve the error. The results show that this tool and methodology can locate and suggest corrections for syntax errors. Our methodology is of practical use to all programmers, but will be especially useful to novices frustrated with incomprehensible syntax errors.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 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 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".