Mining Annotation Usage Rules: A Case Study with MicroProfile
Bibliographic record
Abstract
While Application Programming Interfaces (APIs) allow easier reuse of existing functionality, developers might make mistakes in using these APIs (a.k.a. API misuses). If an API usage specification exists, then automatically detecting such misuses becomes feasible. Since manually encoding specifications is a tedious process, there has been a lot of research regarding pattern-based specification mining. However, while annotations are widely used in Java enterprise microservices frameworks, most of these pattern-based rule discovery techniques have not considered annotation-based API usage rules. In this industrial case study of MicroProfile, an open-source Java microservices framework developed by IBM and others, we investigate whether the idea of pattern-based discovery of rules can be applied to annotation-based API usages. We find that our pattern-based approach mines 23 candidate rules, among which 4 are fully valid specifications and 8 are partially valid specifications. Overall, our technique mines 12 valid rules, 10 of which are not even documented in the official MicroProfile documentation. To evaluate the usefulness of the mined rules, we scan MicroProfile client projects for violations. We find 100 violations of 5 rules in 16 projects. Our results suggest that the mined rules can be useful in detecting and preventing annotation-based API misuses.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".