Detecting unknown inconsistencies in web applications
Bibliographic record
Abstract
Although there has been increasing demand for more reliable web applications, JavaScript bugs abound in web applications. In response to this issue, researchers have proposed automated fault detection tools, which statically analyze the web application code to find bugs. While useful, these tools either only target a limited set of bugs based on predefined rules, or they do not detect bugs caused by cross-language interactions, which occur frequently in web application code. To address this problem, we present an anomaly-based inconsistency detection approach, implemented in a tool called HOLOCRON. The main novelty of our approach is that it does not look for hard-coded inconsistency classes. Instead, it applies subtree pattern matching to infer inconsistency classes and association rule mining to detect inconsistencies that occur both within a single language, and between two languages. We evaluated HOLOCRON, and it successfully detected 51 previously unreported inconsistencies - including 18 bugs and 33 code smells - in 12 web applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".