Unexpected Tokens
Bibliographic record
Abstract
Diagnostic messages generated by compilers and interpreters such as syntax error messages have been researched for decades. Unfortunately these messages which include error, warning, and runtime messages, present substantial difficulty and could be more effective, particularly for novices. Recent years have seen increased number of papers in the area including studies on the effectiveness of these messages, improving or enhancing them, and their usefulness as a part of programming process data that can be used to predict student performance. Despite this increased interest, the long history of literature is quite scattered and has not been brought together in any digestible form. We argue that in order to help the community proceed with more work on diagnostic messages, the literature needs to be presented in a state-of-the-art report. In addition we will synthesize and present the existing evidence for these messages including the difficulties they present and their effectiveness. We will also formulate a set of guidelines based on this evidence that can be used when designing or enhancing diagnostic messages. This work can serve as a starting point for those who wish to conduct research on such messages, those who wish to design better messages or those that aim to measure their effectiveness, more effectively.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.104 |
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".