An Empirical Study on Practicality of Specification Mining Algorithms on\n a Real-world Application
Bibliographic record
Abstract
Dynamic model inference techniques have been the center of many research\nprojects recently. There are now multiple open source implementations of\nstate-of-the-art algorithms, which provide basic abstraction and merging\ncapabilities. Most of these tools and algorithms have been developed with one\nparticular application in mind, which is program comprehension. The outputs\nmodels can abstract away the details of the program and represent the software\nbehavior in a concise and easy to understand form. However, one application\ncontext that is less studied is using such inferred models for debugging, where\nthe behavior to abstract is a faulty behavior (e.g., a set of execution traces\nincluding a failed test case). We tried to apply some of the existing model\ninference techniques (implemented in a promising tool called MINT) in a\nreal-world industrial context to support program comprehension for debugging.\nOur initial experiments have shown many limitations both in terms of\nimplementation as well as the algorithms. The paper will discuss the root cause\nof the failures and proposes ideas for future improvement.\n
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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 | 0.028 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".