Automated repair of HTML generation errors in PHP applications using string constraint solving
Bibliographic record
Abstract
Abstract—PHP web applications routinely generate invalid HTML. Modern browsers silently correct HTML errors, but sometimes malformed pages render inconsistently, cause browser crashes, or expose security vulnerabilities. Fixing errors in generated pages is usually straightforward, but repairing the generating PHP program can be much harder. We observe that malformed HTML is often produced by incorrect constant prints, i.e., statements that print string literals, and present two tools for automatically repairing such HTML generation errors. PHPQuickFix repairs simple bugs by statically analyzing individual prints. PHPRepair handles more general repairs using a dynamic approach. Based on a test suite, the property that all tests should produce their expected output is encoded as a string constraint over variables representing constant prints. Solving this constraint describes how constant prints must be modified to make all tests pass. Both tools were implemented as an Eclipse plugin and evaluated on PHP programs containing hundreds of HTML generation errors, most of which our tools were able to repair automatically. Keywords-PHP; automated repair; string constraints I.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".